A necessary condition for existence of any weakly consistent estimator is presented in nonlinear models with random effects. Under mild regularity conditions, the strong consistency and the asymptotic normality of the...A necessary condition for existence of any weakly consistent estimator is presented in nonlinear models with random effects. Under mild regularity conditions, the strong consistency and the asymptotic normality of the estimator are proved. The results of Breslow and Calyton(1993), Lee and Nelder(1996) are improved.展开更多
Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior chara...Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.展开更多
In some situations,the failure time of interest is defined as the gap time between two related events and the observations on both event times can suffer either right or interval censoring.Such data are usually referr...In some situations,the failure time of interest is defined as the gap time between two related events and the observations on both event times can suffer either right or interval censoring.Such data are usually referred to as doubly censored data and frequently encountered in many clinical and observational studies.Additionally,there may also exist a cured subgroup in the whole population,which means that not every individual under study will experience the failure time of interest eventually.In this paper,we consider regression analysis of doubly censored data with a cured subgroup under a wide class of flexible transformation cure models.Specifically,we consider marginal likelihood estimation and develop a two-step approach by combining the multiple imputation and a new expectation-maximization(EM)algorithm for its implementation.The resulting estimators are shown to be consistent and asymptotically normal.The finite sample performance of the proposed method is investigated through simulation studies.The proposed method is also applied to a real dataset arising from an AIDS cohort study for illustration.展开更多
文摘A necessary condition for existence of any weakly consistent estimator is presented in nonlinear models with random effects. Under mild regularity conditions, the strong consistency and the asymptotic normality of the estimator are proved. The results of Breslow and Calyton(1993), Lee and Nelder(1996) are improved.
文摘Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.
基金Supported by the National Natural Science Foundation of China(Grant Nos.11771431,11690015,11926341,11901128 and 11601097)Key Laboratory of RCSDS,CAS(Grant Nos.2008DP 173182)Natural Science Foundation of Guangdong Province of China(Grant No.2018A030310068)。
文摘In some situations,the failure time of interest is defined as the gap time between two related events and the observations on both event times can suffer either right or interval censoring.Such data are usually referred to as doubly censored data and frequently encountered in many clinical and observational studies.Additionally,there may also exist a cured subgroup in the whole population,which means that not every individual under study will experience the failure time of interest eventually.In this paper,we consider regression analysis of doubly censored data with a cured subgroup under a wide class of flexible transformation cure models.Specifically,we consider marginal likelihood estimation and develop a two-step approach by combining the multiple imputation and a new expectation-maximization(EM)algorithm for its implementation.The resulting estimators are shown to be consistent and asymptotically normal.The finite sample performance of the proposed method is investigated through simulation studies.The proposed method is also applied to a real dataset arising from an AIDS cohort study for illustration.
