Multiple sources localization is an important technique applied in many areasl such as sonar, radar, biomedical, and geology exploration. High resolution localization needs to employ jointly two sorts of high resoluti...Multiple sources localization is an important technique applied in many areasl such as sonar, radar, biomedical, and geology exploration. High resolution localization needs to employ jointly two sorts of high resolution estimators, time-delay estimator and Direction Of Arrival (DOA) estimator, both of which are tough problems attracting many signal processing researchers. There is also another difficulty that is to pair two groups of parameters in timedelay and DOA domains. In underwater environment, multiple sources localization research faces more difficulties because of the long duration emitted wave, limit aperture of array, and short data record of echoes. In this paper, an new extended ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) method is presented. With a single echo wave, both time-delay and DOA parameters of multiple sources are estimated simultaneously.No additional pairing algorithm is needed to obtain the source locations. The performance of the new estimators and the probability of correct pairing is given by computer simulations, andthe results shows that good estimation can be obtained in low SNR (Signal to Noise Ratio) for multiple sources localization.展开更多
Existing papers on human capital and growth in China has been using single equation estimations.This might cause a simultaneity bias if a two-way causality between the two variables exists.In this paper,the author per...Existing papers on human capital and growth in China has been using single equation estimations.This might cause a simultaneity bias if a two-way causality between the two variables exists.In this paper,the author performs vector autoregressive estimations using panel data on the number of graduates at each level of education as a proxy for human capital in China during 1991-2005.The results show that investment in human capital increases output per worker at all three levels of education.Regarding the effects of output per worker on the accumulation of human capital,the author finds mixed results with the primary-school graduates'benefits the most from increases in per capita output.展开更多
Background： Analysing and modelling plant growth is an important interdisciplinary field of plant science. The use of relative growth rates, involving the analysis of plant growth relative to plant size, has more or ...Background： Analysing and modelling plant growth is an important interdisciplinary field of plant science. The use of relative growth rates, involving the analysis of plant growth relative to plant size, has more or less independently emerged in different research groups and at different times and has provided powerful tools for assessing the growth performance and growth efficiency of plants and plant populations. In this paper, we explore how these isolated methods can be combined to form a consistent methodology for modelling relative growth rates. Methods： We review and combine existing methods of analysing and modelling relative growth rates and apply a combination of methods to Sitka spruce （Piceo sitchensis （Bong.） Carr.） stem-analysis data from North Wales （UK） and British Douglas fir （Pseudotsugd menziesii （Mirb.） Franco） yield table data. Results： The results indicate that, by combining the approaches of different plant-growth analysis laboratories and using them simultaneously, we can advance and standardise the concept of relative plant growth. Particularly the growth multiplier plays an important role in modelling relative growth rates. Another useful technique has been the recent introduction of size-standardised relative growth rates. Conclusions： Modelling relative growth rates mainly serves two purposes, 1） an improved analysis of growth performance and efficiency and 2） the prediction of future or past growth rates. This makes the concept of relative growth ideally suited to growth reconstruction as required in dendrochronology, climate change and forest decline research and for interdisciplinary research projects beyond the realm of plant science.展开更多
Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal c...Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.展开更多
Propensity score is widely used to estimate treatment effects in observational studies.The covariate adjustment using propensity score is the most straightforward method in the literature of causal inference.In this a...Propensity score is widely used to estimate treatment effects in observational studies.The covariate adjustment using propensity score is the most straightforward method in the literature of causal inference.In this article,we estimate the survival treatment effect with covariate adjustment using propensity score in the semiparametric accelerated failure time model.We establish the asymptotic properties of the proposed estimator by simultaneous estimating equations.We conduct simulation studies to evaluate the finite sample performance of the proposed method.A real data set from the German Breast Cancer Study Group is analyzed to illustrate the proposed method.展开更多
文摘Multiple sources localization is an important technique applied in many areasl such as sonar, radar, biomedical, and geology exploration. High resolution localization needs to employ jointly two sorts of high resolution estimators, time-delay estimator and Direction Of Arrival (DOA) estimator, both of which are tough problems attracting many signal processing researchers. There is also another difficulty that is to pair two groups of parameters in timedelay and DOA domains. In underwater environment, multiple sources localization research faces more difficulties because of the long duration emitted wave, limit aperture of array, and short data record of echoes. In this paper, an new extended ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) method is presented. With a single echo wave, both time-delay and DOA parameters of multiple sources are estimated simultaneously.No additional pairing algorithm is needed to obtain the source locations. The performance of the new estimators and the probability of correct pairing is given by computer simulations, andthe results shows that good estimation can be obtained in low SNR (Signal to Noise Ratio) for multiple sources localization.
文摘Existing papers on human capital and growth in China has been using single equation estimations.This might cause a simultaneity bias if a two-way causality between the two variables exists.In this paper,the author performs vector autoregressive estimations using panel data on the number of graduates at each level of education as a proxy for human capital in China during 1991-2005.The results show that investment in human capital increases output per worker at all three levels of education.Regarding the effects of output per worker on the accumulation of human capital,the author finds mixed results with the primary-school graduates'benefits the most from increases in per capita output.
文摘Background： Analysing and modelling plant growth is an important interdisciplinary field of plant science. The use of relative growth rates, involving the analysis of plant growth relative to plant size, has more or less independently emerged in different research groups and at different times and has provided powerful tools for assessing the growth performance and growth efficiency of plants and plant populations. In this paper, we explore how these isolated methods can be combined to form a consistent methodology for modelling relative growth rates. Methods： We review and combine existing methods of analysing and modelling relative growth rates and apply a combination of methods to Sitka spruce （Piceo sitchensis （Bong.） Carr.） stem-analysis data from North Wales （UK） and British Douglas fir （Pseudotsugd menziesii （Mirb.） Franco） yield table data. Results： The results indicate that, by combining the approaches of different plant-growth analysis laboratories and using them simultaneously, we can advance and standardise the concept of relative plant growth. Particularly the growth multiplier plays an important role in modelling relative growth rates. Another useful technique has been the recent introduction of size-standardised relative growth rates. Conclusions： Modelling relative growth rates mainly serves two purposes, 1） an improved analysis of growth performance and efficiency and 2） the prediction of future or past growth rates. This makes the concept of relative growth ideally suited to growth reconstruction as required in dendrochronology, climate change and forest decline research and for interdisciplinary research projects beyond the realm of plant science.
基金The National Natural Science Foundation of China(No 60504033)
文摘Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.
基金the National Natural Science Foundation of China(Grant Nos.11501578 and 11701571)the Fundamental Research Funds for the Central Universities(Grant No.31512111206)。
文摘Propensity score is widely used to estimate treatment effects in observational studies.The covariate adjustment using propensity score is the most straightforward method in the literature of causal inference.In this article,we estimate the survival treatment effect with covariate adjustment using propensity score in the semiparametric accelerated failure time model.We establish the asymptotic properties of the proposed estimator by simultaneous estimating equations.We conduct simulation studies to evaluate the finite sample performance of the proposed method.A real data set from the German Breast Cancer Study Group is analyzed to illustrate the proposed method.
