{"title":"On Combining Individual-Level Data With Summary Data in Statistical Inferences","authors":"Lu Deng, Sheng Fu, J. Qin, Kai Yu","doi":"10.5705/ss.202022.0228","DOIUrl":"https://doi.org/10.5705/ss.202022.0228","url":null,"abstract":"On Combining Individual-Level Data With Summary Data in Statistical Inferences","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"34 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70939106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: Motivated by studying the asymptotic properties of the parameter estimator in switching linear state space models, switching GARCH models, switching stochastic volatility models, and recurrent neural networks, we investigate the maximum likelihood estimator for general Markov switching models. To this end, we first propose an innovative matrix-valued Markovian iterated function system (MIFS) representation for the likelihood function. Then, we express the derivatives of the MIFS as a composition of random matrices. To the best of our knowledge, this is a new method in the literature. Using this useful device, we establish the strong consistency and asymptotic normality of the maximum likelihood estimator under some regularity conditions. Furthermore, we characterize the Fisher information as the inverse of the asymptotic variance.
{"title":"Asymptotic Behavior of the Maximum Likelihood Estimator for General Markov Switching Models","authors":"C. Fuh, T. Pang","doi":"10.5705/ss.202021.0336","DOIUrl":"https://doi.org/10.5705/ss.202021.0336","url":null,"abstract":": Motivated by studying the asymptotic properties of the parameter estimator in switching linear state space models, switching GARCH models, switching stochastic volatility models, and recurrent neural networks, we investigate the maximum likelihood estimator for general Markov switching models. To this end, we first propose an innovative matrix-valued Markovian iterated function system (MIFS) representation for the likelihood function. Then, we express the derivatives of the MIFS as a composition of random matrices. To the best of our knowledge, this is a new method in the literature. Using this useful device, we establish the strong consistency and asymptotic normality of the maximum likelihood estimator under some regularity conditions. Furthermore, we characterize the Fisher information as the inverse of the asymptotic variance.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"23 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Threshold Estimation in Proportional Mean Residual Life Model","authors":"Bing Wang, Xinyuan Song","doi":"10.5705/ss.202022.0017","DOIUrl":"https://doi.org/10.5705/ss.202022.0017","url":null,"abstract":"of the main","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Homogeneity Tests for High-dimensional Mean Vectors and Covariance Matrices","authors":"Wenwen Guo, Xinyuan Song, H. Cui","doi":"10.5705/ss.202022.0048","DOIUrl":"https://doi.org/10.5705/ss.202022.0048","url":null,"abstract":"Homogeneity Tests","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This supplementary materials provides detailed proofs of Theorem 1 and 2 and Proposition 1-3 as well as the power simulation results under the heteroscedastic condition.
{"title":"An Adaptive Weighted Component Test for High-Dimensional Means","authors":"Yidi Qu, L. Shu, Jinfeng Xu","doi":"10.5705/ss.202022.0143","DOIUrl":"https://doi.org/10.5705/ss.202022.0143","url":null,"abstract":"This supplementary materials provides detailed proofs of Theorem 1 and 2 and Proposition 1-3 as well as the power simulation results under the heteroscedastic condition.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Semiparametric Reversed Mean Model for Recurrent Event Process with Informative Terminal Event Wen Su1∗ , Li Liu2∗, Guosheng Yin, Xingqiu Zhao and Ying Zhang Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong School of Mathematics and Statistics, Wuhan University, Wuhan, China Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA
{"title":"Semiparametric Reversed Mean Model for Recurrent Event Process with Informative Terminal Event","authors":"Wen Su, Li Liu, G. Yin, Xingqiu Zhao, Ying Zhang","doi":"10.5705/ss.202021.0353","DOIUrl":"https://doi.org/10.5705/ss.202021.0353","url":null,"abstract":"Semiparametric Reversed Mean Model for Recurrent Event Process with Informative Terminal Event Wen Su1∗ , Li Liu2∗, Guosheng Yin, Xingqiu Zhao and Ying Zhang Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong School of Mathematics and Statistics, Wuhan University, Wuhan, China Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: In this paper, we propose an outlier detection procedure, based on a high-breakdown minimum ridge covariance determinant estimator that is especially useful for the large p/n scenario. The estimator is obtained from the subset of observations, after excluding potential outliers, by applying the so-called concentration steps. We explore the asymptotic distribution of the modified Mahalanobis distance related to the proposed estimator under certain moment conditions, and obtain a theoretical cutoff value for outlier identification. We also improve the outlier detection power by adding a one-step reweighting procedure. Lastly, we investigate the performance of the proposed methods using simulations and a real-data analysis.
{"title":"Outlier Detection via a Minimum Ridge Covariance Determinant Estimator","authors":"Chikun Li, B. Jin, Yuehua Wu","doi":"10.5705/ss.202022.0142","DOIUrl":"https://doi.org/10.5705/ss.202022.0142","url":null,"abstract":": In this paper, we propose an outlier detection procedure, based on a high-breakdown minimum ridge covariance determinant estimator that is especially useful for the large p/n scenario. The estimator is obtained from the subset of observations, after excluding potential outliers, by applying the so-called concentration steps. We explore the asymptotic distribution of the modified Mahalanobis distance related to the proposed estimator under certain moment conditions, and obtain a theoretical cutoff value for outlier identification. We also improve the outlier detection power by adding a one-step reweighting procedure. Lastly, we investigate the performance of the proposed methods using simulations and a real-data analysis.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On the Efficiency of Composite Likelihood Estimation for Gaussian Spatial Processes","authors":"N. Chua, Francis K. C. Hui, A. Welsh","doi":"10.5705/ss.202020.0311","DOIUrl":"https://doi.org/10.5705/ss.202020.0311","url":null,"abstract":"the Efficiency of Composite Likelihood","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70936712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: In comparative studies, researchers often seek an optimal covariate balance. However, chance imbalance still exists in randomized experiments, and becomes more serious as the number of covariates increases. To address this issue, we introduce a new randomization procedure, called adaptive randomization via the Mahalanobis distance (ARM). The proposed method allocates units sequentially and adaptively, using information on the current level of imbalance and the incoming unit’s covariate. Theoretical results and numerical comparison show that with a large number of covariates or a large number of units, the proposed method shows substantial advantages over traditional methods in terms of the covariate balance, estimation accuracy, hypothesis testing power, and computational time. The proposed method attains the optimal covariate balance, in the sense that the estimated treatment effect attains its minimum variance asymptotically, and can be applied in both causal inference and clinical trials. Lastly, numerical stud-1
{"title":"Adaptive Randomization via Mahalanobis Distance","authors":"Yichen Qin, Y. Li, Wei Ma, Haoyu Yang, F. Hu","doi":"10.5705/ss.202020.0440","DOIUrl":"https://doi.org/10.5705/ss.202020.0440","url":null,"abstract":": In comparative studies, researchers often seek an optimal covariate balance. However, chance imbalance still exists in randomized experiments, and becomes more serious as the number of covariates increases. To address this issue, we introduce a new randomization procedure, called adaptive randomization via the Mahalanobis distance (ARM). The proposed method allocates units sequentially and adaptively, using information on the current level of imbalance and the incoming unit’s covariate. Theoretical results and numerical comparison show that with a large number of covariates or a large number of units, the proposed method shows substantial advantages over traditional methods in terms of the covariate balance, estimation accuracy, hypothesis testing power, and computational time. The proposed method attains the optimal covariate balance, in the sense that the estimated treatment effect attains its minimum variance asymptotically, and can be applied in both causal inference and clinical trials. Lastly, numerical stud-1","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70936861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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