Consider the semiparametric varying-coefficient heteroscedastic partially linear model Yi = X^T i β＋ Z^T iα（Ti） ＋ σiei, 1 ≤ i≤ n, where σ ^2i= f（Ui）, β is a p × 1 column vector of unknown parameter, ...Consider the semiparametric varying-coefficient heteroscedastic partially linear model Yi = X^T i β＋ Z^T iα（Ti） ＋ σiei, 1 ≤ i≤ n, where σ ^2i= f（Ui）, β is a p × 1 column vector of unknown parameter, （Xi, Zi, Ti, Ui） are random design q-dimensional vector of unknown functions, el points, Yi are the response variables, α（-） is a are random errors. For both cases that f（.） is known and unknown, we propose the empirical log-likelihood ratio statistics for the parameter f（.）. For each case, a nonparametric version of Wilks＇ theorem is derived. The results are then used to construct confidence regions of the parameter. Simulation studies are carried out to assess the performance of the empirical likelihood method.展开更多
The paper considers a multivariate partially linear model under independent errors,and investigates the asymptotic bias and variance-covariance for parametric component βand nonparametric component F（·）by the ...The paper considers a multivariate partially linear model under independent errors,and investigates the asymptotic bias and variance-covariance for parametric component βand nonparametric component F（·）by the GJS estimator and Kernel estimation.展开更多
In this paper, we consider the variable selection for the parametric components of varying coefficient partially linear models with censored data. By constructing a penalized auxiliary vector ingeniously, we propose a...In this paper, we consider the variable selection for the parametric components of varying coefficient partially linear models with censored data. By constructing a penalized auxiliary vector ingeniously, we propose an empirical likelihood based variable selection procedure, and show that it is consistent and satisfies the sparsity. The simulation studies show that the proposed variable selection method is workable.展开更多
This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） prop...This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） proposed a profile least squares estimator for the parametric component and established its asymptotic normality. We further show that the profile least squares estimator can achieve the law of iterated logarithm. Moreover, we study the estimators of the functions characterizing the non-linear part as well as the error variance. The strong convergence rate and the law of iterated logarithm are derived for them, respectively.展开更多
In this article, we study the variable selection of partially linear single-index model(PLSIM). Based on the minimized average variance estimation, the variable selection of PLSIM is done by minimizing average varianc...In this article, we study the variable selection of partially linear single-index model(PLSIM). Based on the minimized average variance estimation, the variable selection of PLSIM is done by minimizing average variance with adaptive l1 penalty. Implementation algorithm is given. Under some regular conditions, we demonstrate the oracle properties of aLASSO procedure for PLSIM. Simulations are used to investigate the effectiveness of the proposed method for variable selection of PLSIM.展开更多
Consider the regression model, n. Here the design points (xi,ti) are known and nonrandom, and ei are random errors. The family of nonparametric estimates of g() including known estimates proposed by Gasser & Mulle...Consider the regression model, n. Here the design points (xi,ti) are known and nonrandom, and ei are random errors. The family of nonparametric estimates of g() including known estimates proposed by Gasser & Muller[1] is also proposed to be a class of new nearest neighbor estimates of g(). Baed on the nonparametric regression procedures, we investigate a statistic for testing H0:g=0, and obtain some aspoptotic results about estimates.展开更多
Variable selection plays an important role in high-dimensional data analysis.But the high-dimensional data often induces the strongly correlated variables problem,which should be properly handled.In this paper,we prop...Variable selection plays an important role in high-dimensional data analysis.But the high-dimensional data often induces the strongly correlated variables problem,which should be properly handled.In this paper,we propose Elastic Net procedure for partially linear models and prove the group effect of its estimate.A simulation study shows that the Elastic Net procedure deals with the strongly correlated variables problem better than the Lasso,ALasso and the Ridge do.Based on the real world data study,we can get that the Elastic Net procedure is particularly useful when the number of predictors pffis much bigger than the sample size n.展开更多
Consider the partly linear model Y = xβ ＋ g（t） ＋ e where the explanatory x is erroneously measured, and both t and the response Y are measured exactly, the random error e is a martingale difference sequence. Let ...Consider the partly linear model Y = xβ ＋ g（t） ＋ e where the explanatory x is erroneously measured, and both t and the response Y are measured exactly, the random error e is a martingale difference sequence. Let ~ be a surrogate variable observed instead of the true x in the primary survey data. Assume that in addition to the primary data set containing N observations of {（Yj, xj, tj）n＋N j=n＋1 }, the independent validation data containing n observations of {（xj, x j, tj）n j=1 } is available. In this paper, a semiparametric method with the primary data is employed to obtain the estimator ofβ and g（-） based on the least squares criterion with the help of validation data. The proposed estimators are proved to be strongly consistent. Finite sample behavior of the estimators is investigated via simulations too.展开更多
We propose the test statistic to check whether the nonpararnetric functions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alte...We propose the test statistic to check whether the nonpararnetric functions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alternative by the local linear method, where we ignore the parametric components, and then estimate the parameters by the two stage method. The test statistic is derived, and it is shown to be asymptotically normal under the null hypothesis.展开更多
This article concerded with a semiparametric generalized partial linear model （GPLM） with the type Ⅱ censored data. A sieve maximum likelihood estimator （MLE） is proposed to estimate the parameter component, allo...This article concerded with a semiparametric generalized partial linear model （GPLM） with the type Ⅱ censored data. A sieve maximum likelihood estimator （MLE） is proposed to estimate the parameter component, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically normal and efficient, and the estimator of the nonparametric function has an optimal convergence rate.展开更多
BACKGROUND： Electrical stimulation kindling model, having epilepsy-inducing and spontaneous seizure and other advantages, is a very ideal experimental animal model. But the kindling effect might be different at diffe...BACKGROUND： Electrical stimulation kindling model, having epilepsy-inducing and spontaneous seizure and other advantages, is a very ideal experimental animal model. But the kindling effect might be different at different sites. OBJECTIVE： To compare the features of animal models of complex partial epilepsy established through unilateral, bilateral and alternate-side kindling at hippocampus and successful rate of modeling among these 3 different ways. DESIGN： A randomized and controlled animal experiment SETTING： Department of Neurology, Qilu Hospital, Shandong University MATERIALS： Totally 60 healthy adult Wistar rats, weighing 200 to 300 g, of either gender, were used in this experiment. BL-410 biological functional experimental system （Taimeng Science and Technology Co. Ltd, Chengdu） and SE-7102 type electronic stimulator （Guangdian Company, Japan） were used in the experiment. METHODS： This experiment was carried out in the Experimental Animal Center of Shandong University from April to June 2004. After rats were anesthetized, electrode was implanted into the hippocampus. From the first day of measurement of afterdischarge threshold value, rats were given two-square-wave suprathreshold stimulation once per day with 400 μA intensity, 1ms wave length, 60 Hz frequency for 1 s duration. Left hippocampus was stimulated in unilateral kindling group, bilateral hippocampi were stimulated in bilateral kindling group, and left and right hippocampi were stimulated alternately every day in the alternate-side kindling group. Seizure intensity was scored： grade 0： normal, 1： wet dog-like shivering, facial spasm, such as, winking, touching the beard, rhythmic chewing and so on; 2： rhythmic nodding; 3： forelimb spasm;4： standing accompanied by bilateral forelimb spasm;5： tumbling, losing balance, four limbs spasm. Modeling was successful when seizure intensity reached grade 5. t test was used for the comparison of mean value between two samples. MAIN OUTCOME MEASURES： Comparison of the successful rate of modeling, the times of stimulation to reach intensity of grade 5, the lasting time of seizure of grade 3 of rats in each group. RESULTS： Four rats of alternate-side kindling group dropped out due to infection-induced electrode loss, and 56 rats were involved in the result analysis. The successful rate of unilateral kindling group, bilateral kin- dling group and alternate-side kindling group was 55%（11/20）,100%（16/16）and 100%（20/20）, respective- ly. The stimuli to reach the grade 5 spasm were significantly more in the bilateral kindling group than in the unilateral kindling group [（30.63±3.48）, （19.36±3.47）times, t=8.268, P 〈 0.01], and those were significantly fewer in the alternate-side kindling group than in the unilateral kindling group [（ 10.85±1.98）times, t=-8.744, P 〈 0.01]. The duration of grade 3 spasm was significantly longer in the bilateral kindling group than in the unilateral kindling group [（9.75±2.59）, （3.21 ±1.58）days,t=-8.183,P 〈 0.01], Among 20 successful rats of al- ternate-side kindling group, grade 5 spasm was found in the left hippocampi of 11 rats, but grade 3 spasm in their right hippocampi; Grade 5 spasm was found in the right hippocampi of the other 9 rats, grade 4 spasm in the left hippocampus of 1 rat and grade 3 of 8 rats. CONCLUSION ： The speed of establishing epilepsy seizure model by alternate-side kindling is faster than that by unilateral kindling, while that by bilateral kindling is slower than that by unilateral kindling. The successful rate is very high to establish complex partial epilepsy with alternate-side or bilateral kindling. Epilepsy seizure established by alternate-side kindling has antagonistic effect of kindling and the seizure duration of grade 3 spasm is prolonged.展开更多
Partial cooperation models are studied for many years to solve the bilevel programming problems where the follower’s optimal reaction is not unique. However, in these existed models, the follower’s cooperation level...Partial cooperation models are studied for many years to solve the bilevel programming problems where the follower’s optimal reaction is not unique. However, in these existed models, the follower’s cooperation level does not depend on the leader’s decision. A new model is proposed to solve this deficiency. It is proved the feasibility of the new model when the reaction set of the lower level is lower semicontinuous. And the numerical results show that the new model has optimal solutions when the reaction set of the lower level is discrete, lower semi-continuous and non-lower semi-continuous.展开更多
When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To ...When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To solve this problem,we propose to add the nonparametric part(systematic errors)to the partial EIV model,and build the partial EIV model to weaken the influence of systematic errors.Then,having rewritten the model as a nonlinear model,we derive the formula of parameter estimations based on the penalized total least squares criterion.Furthermore,based on the second-order approximation method of precision estimation,we derive the second-order bias and covariance of parameter estimations and calculate the mean square error(MSE).Aiming at the selection of the smoothing factor,we propose to use the U curve method.The experiments show that the proposed method can mitigate the influence of systematic errors to a certain extent compared with the traditional method and get more reliable parameter estimations and its precision information,which validates the feasibility and effectiveness of the proposed method.展开更多
This paper considers the local linear regression estimators for partially linear model with censored data. Which have some nice large-sample behaviors and are easy to implement. By many simulation runs, the author als...This paper considers the local linear regression estimators for partially linear model with censored data. Which have some nice large-sample behaviors and are easy to implement. By many simulation runs, the author also found that the estimators show remarkable in the small sample case yet.展开更多
In this article, a partially linear single-index model /or longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be est...In this article, a partially linear single-index model /or longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be estimated simultaneously by the proposed method while the feature of longitudinal data is considered. The existence, strong consistency and asymptotic normality of the estimators are proved under suitable conditions. A simulation study is conducted to investigate the finite sample performance of the proposed method. Our approach can also be used to study the pure single-index model for longitudinal data.展开更多
On the basis of a detailed discussion of the development of total ionizing dose （TID） effect model, a new commercial-model-independent TID modeling approach for partially depleted silicon-on-insulator metal-oxide- s...On the basis of a detailed discussion of the development of total ionizing dose （TID） effect model, a new commercial-model-independent TID modeling approach for partially depleted silicon-on-insulator metal-oxide- semiconductor field effect transistors is developed. An exponential approximation is proposed to simplify the trap charge calculation. Irradiation experiments with 60Co gamma rays for IO and core devices are performed to validate the simulation results. An excellent agreement of measurement with the simulation results is observed.展开更多
A new particle deposition model, namely partial deposition model, is developed in order to improve the accuracy of prediction to particle deposition. Concepts of critical velocity and critical angle are proposed and u...A new particle deposition model, namely partial deposition model, is developed in order to improve the accuracy of prediction to particle deposition. Concepts of critical velocity and critical angle are proposed and used to determine whether particles are deposited or not. The comparison of numerical results calculated by partial deposition model and existing deposition model shows that the deposition distribution obtained by partial deposition model is more reasonable. Based on the predicted deposition results, the change of total pressure loss coefficient with operating time and the distribution of pressure coefficients on blade surface after 500 hours are predicted by using partial deposition model.展开更多
Consider tile partial linear model Y=Xβ+ g(T) + e. Wilers Y is at risk of being censored from the right, g is an unknown smoothing function on [0,1], β is a 1-dimensional parameter to be estimated and e is an unobse...Consider tile partial linear model Y=Xβ+ g(T) + e. Wilers Y is at risk of being censored from the right, g is an unknown smoothing function on [0,1], β is a 1-dimensional parameter to be estimated and e is an unobserved error. In Ref[1,2], it wes proved that the estimator for the asymptotic variance of βn(βn) is consistent. In this paper, we establish the limit distribution and the law of the iterated logarithm for,En, and obtain the convergest rates for En and the strong uniform convergent rates for gn(gn).展开更多
The deformation prediction models of Wuqiangxi concrete gravity dam are developed,including two statistical models and a deep learning model.In the statistical models,the reliable monitoring data are firstly determine...The deformation prediction models of Wuqiangxi concrete gravity dam are developed,including two statistical models and a deep learning model.In the statistical models,the reliable monitoring data are firstly determined with Lahitte criterion;then,the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data,and the factors of water pressure,temperature and time effect are considered in the models;finally,according to the monitoring data from 2006 to 2020 of five typical measuring points including J23(on dam section 24^(#)),J33(on dam section 4^(#)),J35(on dam section 8^(#)),J37(on dam section 12^(#)),and J39(on dam section 15^(#))located on the crest of Wuqiangxi concrete gravity dam,the settlement curves of the measuring points are obtained with the stepwise regression and partial least squares regression models.A deep learning model is developed based on long short-term memory(LSTM)recurrent neural network.In the LSTM model,two LSTMlayers are used,the rectified linear unit function is adopted as the activation function,the input sequence length is 20,and the random search is adopted.The monitoring data for the five typical measuring points from 2006 to 2017 are selected as the training set,and the monitoring data from 2018 to 2020 are taken as the test set.From the results of case study,we can find that(1)the good fitting results can be obtained with the two statistical models;(2)the partial least squares regression algorithm can solve the model with high correlation factors and reasonably explain the factors;(3)the prediction accuracy of the LSTM model increases with increasing the amount of training data.In the deformation prediction of concrete gravity dam,the LSTM model is suggested when there are sufficient training data,while the partial least squares regression method is suggested when the training data are insufficient.展开更多
Consider the regression model Y=Xβ+ g(T) + e. Here g is an unknown smoothing function on [0, 1], β is a l-dimensional parameter to be estimated, and e is an unobserved error. When data are randomly censored, the est...Consider the regression model Y=Xβ+ g(T) + e. Here g is an unknown smoothing function on [0, 1], β is a l-dimensional parameter to be estimated, and e is an unobserved error. When data are randomly censored, the estimators βn* and gn*forβ and g are obtained by using class K and the least square methods. It is shown that βn* is asymptotically normal and gn* achieves the convergent rate O(n-1/3).展开更多
基金Supported by the National Natural Science Foundation of China (Grant No. 71171003)Natural Science Research Project of Anhui Provincial Colleges (Grant No. KJ2011A032)+3 种基金Anhui Polytechnic University Foundation for Recruiting Talent (Grant Nos. 2011YQQ0042009YQQ005)Young Teachers Science Research Foundation of Anhui Polytechnic University (Grant No. 2009YQ035)Anhui Provincial Natural Science Foundation
文摘Consider the semiparametric varying-coefficient heteroscedastic partially linear model Yi = X^T i β＋ Z^T iα（Ti） ＋ σiei, 1 ≤ i≤ n, where σ ^2i= f（Ui）, β is a p × 1 column vector of unknown parameter, （Xi, Zi, Ti, Ui） are random design q-dimensional vector of unknown functions, el points, Yi are the response variables, α（-） is a are random errors. For both cases that f（.） is known and unknown, we propose the empirical log-likelihood ratio statistics for the parameter f（.）. For each case, a nonparametric version of Wilks＇ theorem is derived. The results are then used to construct confidence regions of the parameter. Simulation studies are carried out to assess the performance of the empirical likelihood method.
基金Supported by the Anhui Provincial Natural Science Foundation（11040606M04） Supported by the National Natural Science Foundation of China（10871001,10971097）
文摘The paper considers a multivariate partially linear model under independent errors,and investigates the asymptotic bias and variance-covariance for parametric component βand nonparametric component F（·）by the GJS estimator and Kernel estimation.
基金Supported by the National Natural Science Foundation of China(Grant Nos.1110111911126332)+2 种基金the National Social Science Foundation of China(Grant No.11CTJ004)the Natural Science Foundation of Guangxi Province(Grant No.2010GXNSFB013051)the Philosophy and Social Sciences Foundation of Guangxi Province(Grant No.11FTJ002)
文摘In this paper, we consider the variable selection for the parametric components of varying coefficient partially linear models with censored data. By constructing a penalized auxiliary vector ingeniously, we propose an empirical likelihood based variable selection procedure, and show that it is consistent and satisfies the sparsity. The simulation studies show that the proposed variable selection method is workable.
基金supported by the National Natural Science Funds for Distinguished Young Scholar (70825004)National Natural Science Foundation of China (NSFC) (10731010 and 10628104)+3 种基金the National Basic Research Program (2007CB814902)Creative Research Groups of China (10721101)Leading Academic Discipline Program, the 10th five year plan of 211 Project for Shanghai University of Finance and Economics211 Project for Shanghai University of Financeand Economics (the 3rd phase)
文摘This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） proposed a profile least squares estimator for the parametric component and established its asymptotic normality. We further show that the profile least squares estimator can achieve the law of iterated logarithm. Moreover, we study the estimators of the functions characterizing the non-linear part as well as the error variance. The strong convergence rate and the law of iterated logarithm are derived for them, respectively.
文摘In this article, we study the variable selection of partially linear single-index model(PLSIM). Based on the minimized average variance estimation, the variable selection of PLSIM is done by minimizing average variance with adaptive l1 penalty. Implementation algorithm is given. Under some regular conditions, we demonstrate the oracle properties of aLASSO procedure for PLSIM. Simulations are used to investigate the effectiveness of the proposed method for variable selection of PLSIM.
文摘Consider the regression model, n. Here the design points (xi,ti) are known and nonrandom, and ei are random errors. The family of nonparametric estimates of g() including known estimates proposed by Gasser & Muller[1] is also proposed to be a class of new nearest neighbor estimates of g(). Baed on the nonparametric regression procedures, we investigate a statistic for testing H0:g=0, and obtain some aspoptotic results about estimates.
基金Supported by National Natural Science Foundation of China(No.71462002)the Project for Teaching Reform of Guangxi(GXZZJG2017B084)the Project for Fostering Distinguished Youth Scholars of Guangxi(2020KY50012)。
文摘Variable selection plays an important role in high-dimensional data analysis.But the high-dimensional data often induces the strongly correlated variables problem,which should be properly handled.In this paper,we propose Elastic Net procedure for partially linear models and prove the group effect of its estimate.A simulation study shows that the Elastic Net procedure deals with the strongly correlated variables problem better than the Lasso,ALasso and the Ridge do.Based on the real world data study,we can get that the Elastic Net procedure is particularly useful when the number of predictors pffis much bigger than the sample size n.
基金Supported by National Natural Science Foundation of China(Grant Nos.1127115511371168+7 种基金110011051107112611071269)Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20110061110003)the Natural Science Foundation of Jilin Province(Grant Nos.20130101066JC20130522102JH20101596)"Twelfth Five-Year Plan"Science and Technology Research Project of the Education Department of Jilin Province(Grant No.2012186)
文摘Consider the partly linear model Y = xβ ＋ g（t） ＋ e where the explanatory x is erroneously measured, and both t and the response Y are measured exactly, the random error e is a martingale difference sequence. Let ~ be a surrogate variable observed instead of the true x in the primary survey data. Assume that in addition to the primary data set containing N observations of {（Yj, xj, tj）n＋N j=n＋1 }, the independent validation data containing n observations of {（xj, x j, tj）n j=1 } is available. In this paper, a semiparametric method with the primary data is employed to obtain the estimator ofβ and g（-） based on the least squares criterion with the help of validation data. The proposed estimators are proved to be strongly consistent. Finite sample behavior of the estimators is investigated via simulations too.
文摘We propose the test statistic to check whether the nonpararnetric functions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alternative by the local linear method, where we ignore the parametric components, and then estimate the parameters by the two stage method. The test statistic is derived, and it is shown to be asymptotically normal under the null hypothesis.
基金The talent research fund launched (3004-893325) of Dalian University of Technologythe NNSF (10271049) of China.
文摘This article concerded with a semiparametric generalized partial linear model （GPLM） with the type Ⅱ censored data. A sieve maximum likelihood estimator （MLE） is proposed to estimate the parameter component, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically normal and efficient, and the estimator of the nonparametric function has an optimal convergence rate.
文摘BACKGROUND： Electrical stimulation kindling model, having epilepsy-inducing and spontaneous seizure and other advantages, is a very ideal experimental animal model. But the kindling effect might be different at different sites. OBJECTIVE： To compare the features of animal models of complex partial epilepsy established through unilateral, bilateral and alternate-side kindling at hippocampus and successful rate of modeling among these 3 different ways. DESIGN： A randomized and controlled animal experiment SETTING： Department of Neurology, Qilu Hospital, Shandong University MATERIALS： Totally 60 healthy adult Wistar rats, weighing 200 to 300 g, of either gender, were used in this experiment. BL-410 biological functional experimental system （Taimeng Science and Technology Co. Ltd, Chengdu） and SE-7102 type electronic stimulator （Guangdian Company, Japan） were used in the experiment. METHODS： This experiment was carried out in the Experimental Animal Center of Shandong University from April to June 2004. After rats were anesthetized, electrode was implanted into the hippocampus. From the first day of measurement of afterdischarge threshold value, rats were given two-square-wave suprathreshold stimulation once per day with 400 μA intensity, 1ms wave length, 60 Hz frequency for 1 s duration. Left hippocampus was stimulated in unilateral kindling group, bilateral hippocampi were stimulated in bilateral kindling group, and left and right hippocampi were stimulated alternately every day in the alternate-side kindling group. Seizure intensity was scored： grade 0： normal, 1： wet dog-like shivering, facial spasm, such as, winking, touching the beard, rhythmic chewing and so on; 2： rhythmic nodding; 3： forelimb spasm;4： standing accompanied by bilateral forelimb spasm;5： tumbling, losing balance, four limbs spasm. Modeling was successful when seizure intensity reached grade 5. t test was used for the comparison of mean value between two samples. MAIN OUTCOME MEASURES： Comparison of the successful rate of modeling, the times of stimulation to reach intensity of grade 5, the lasting time of seizure of grade 3 of rats in each group. RESULTS： Four rats of alternate-side kindling group dropped out due to infection-induced electrode loss, and 56 rats were involved in the result analysis. The successful rate of unilateral kindling group, bilateral kin- dling group and alternate-side kindling group was 55%（11/20）,100%（16/16）and 100%（20/20）, respective- ly. The stimuli to reach the grade 5 spasm were significantly more in the bilateral kindling group than in the unilateral kindling group [（30.63±3.48）, （19.36±3.47）times, t=8.268, P 〈 0.01], and those were significantly fewer in the alternate-side kindling group than in the unilateral kindling group [（ 10.85±1.98）times, t=-8.744, P 〈 0.01]. The duration of grade 3 spasm was significantly longer in the bilateral kindling group than in the unilateral kindling group [（9.75±2.59）, （3.21 ±1.58）days,t=-8.183,P 〈 0.01], Among 20 successful rats of al- ternate-side kindling group, grade 5 spasm was found in the left hippocampi of 11 rats, but grade 3 spasm in their right hippocampi; Grade 5 spasm was found in the right hippocampi of the other 9 rats, grade 4 spasm in the left hippocampus of 1 rat and grade 3 of 8 rats. CONCLUSION ： The speed of establishing epilepsy seizure model by alternate-side kindling is faster than that by unilateral kindling, while that by bilateral kindling is slower than that by unilateral kindling. The successful rate is very high to establish complex partial epilepsy with alternate-side or bilateral kindling. Epilepsy seizure established by alternate-side kindling has antagonistic effect of kindling and the seizure duration of grade 3 spasm is prolonged.
基金supported by the National Natural Science Foundationof China (70771080)the National Science Foundation of Hubei Province(20091107)Hubei Province Key Laboratory of Systems Science in Metallurgical Process (B201003)
文摘Partial cooperation models are studied for many years to solve the bilevel programming problems where the follower’s optimal reaction is not unique. However, in these existed models, the follower’s cooperation level does not depend on the leader’s decision. A new model is proposed to solve this deficiency. It is proved the feasibility of the new model when the reaction set of the lower level is lower semicontinuous. And the numerical results show that the new model has optimal solutions when the reaction set of the lower level is discrete, lower semi-continuous and non-lower semi-continuous.
基金supported by the National Natural Science Foundation of China,Nos.41874001 and 41664001Support Program for Outstanding Youth Talents in Jiangxi Province,No.20162BCB23050National Key Research and Development Program,No.2016YFB0501405。
文摘When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To solve this problem,we propose to add the nonparametric part(systematic errors)to the partial EIV model,and build the partial EIV model to weaken the influence of systematic errors.Then,having rewritten the model as a nonlinear model,we derive the formula of parameter estimations based on the penalized total least squares criterion.Furthermore,based on the second-order approximation method of precision estimation,we derive the second-order bias and covariance of parameter estimations and calculate the mean square error(MSE).Aiming at the selection of the smoothing factor,we propose to use the U curve method.The experiments show that the proposed method can mitigate the influence of systematic errors to a certain extent compared with the traditional method and get more reliable parameter estimations and its precision information,which validates the feasibility and effectiveness of the proposed method.
文摘This paper considers the local linear regression estimators for partially linear model with censored data. Which have some nice large-sample behaviors and are easy to implement. By many simulation runs, the author also found that the estimators show remarkable in the small sample case yet.
基金Supported by the National Natural Science Foundation of China (10571008)the Natural Science Foundation of Henan (092300410149)the Core Teacher Foundationof Henan (2006141)
文摘In this article, a partially linear single-index model /or longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be estimated simultaneously by the proposed method while the feature of longitudinal data is considered. The existence, strong consistency and asymptotic normality of the estimators are proved under suitable conditions. A simulation study is conducted to investigate the finite sample performance of the proposed method. Our approach can also be used to study the pure single-index model for longitudinal data.
基金Supported by the National Natural Science Foundation of China under Grant Nos 61404151 and 61574153
文摘On the basis of a detailed discussion of the development of total ionizing dose （TID） effect model, a new commercial-model-independent TID modeling approach for partially depleted silicon-on-insulator metal-oxide- semiconductor field effect transistors is developed. An exponential approximation is proposed to simplify the trap charge calculation. Irradiation experiments with 60Co gamma rays for IO and core devices are performed to validate the simulation results. An excellent agreement of measurement with the simulation results is observed.
文摘A new particle deposition model, namely partial deposition model, is developed in order to improve the accuracy of prediction to particle deposition. Concepts of critical velocity and critical angle are proposed and used to determine whether particles are deposited or not. The comparison of numerical results calculated by partial deposition model and existing deposition model shows that the deposition distribution obtained by partial deposition model is more reasonable. Based on the predicted deposition results, the change of total pressure loss coefficient with operating time and the distribution of pressure coefficients on blade surface after 500 hours are predicted by using partial deposition model.
文摘Consider tile partial linear model Y=Xβ+ g(T) + e. Wilers Y is at risk of being censored from the right, g is an unknown smoothing function on [0,1], β is a 1-dimensional parameter to be estimated and e is an unobserved error. In Ref[1,2], it wes proved that the estimator for the asymptotic variance of βn(βn) is consistent. In this paper, we establish the limit distribution and the law of the iterated logarithm for,En, and obtain the convergest rates for En and the strong uniform convergent rates for gn(gn).
文摘The deformation prediction models of Wuqiangxi concrete gravity dam are developed,including two statistical models and a deep learning model.In the statistical models,the reliable monitoring data are firstly determined with Lahitte criterion;then,the stepwise regression and partial least squares regression models for deformation prediction of concrete gravity dam are constructed in terms of the reliable monitoring data,and the factors of water pressure,temperature and time effect are considered in the models;finally,according to the monitoring data from 2006 to 2020 of five typical measuring points including J23(on dam section 24^(#)),J33(on dam section 4^(#)),J35(on dam section 8^(#)),J37(on dam section 12^(#)),and J39(on dam section 15^(#))located on the crest of Wuqiangxi concrete gravity dam,the settlement curves of the measuring points are obtained with the stepwise regression and partial least squares regression models.A deep learning model is developed based on long short-term memory(LSTM)recurrent neural network.In the LSTM model,two LSTMlayers are used,the rectified linear unit function is adopted as the activation function,the input sequence length is 20,and the random search is adopted.The monitoring data for the five typical measuring points from 2006 to 2017 are selected as the training set,and the monitoring data from 2018 to 2020 are taken as the test set.From the results of case study,we can find that(1)the good fitting results can be obtained with the two statistical models;(2)the partial least squares regression algorithm can solve the model with high correlation factors and reasonably explain the factors;(3)the prediction accuracy of the LSTM model increases with increasing the amount of training data.In the deformation prediction of concrete gravity dam,the LSTM model is suggested when there are sufficient training data,while the partial least squares regression method is suggested when the training data are insufficient.
文摘Consider the regression model Y=Xβ+ g(T) + e. Here g is an unknown smoothing function on [0, 1], β is a l-dimensional parameter to be estimated, and e is an unobserved error. When data are randomly censored, the estimators βn* and gn*forβ and g are obtained by using class K and the least square methods. It is shown that βn* is asymptotically normal and gn* achieves the convergent rate O(n-1/3).
