Statistical Methodology最新文献

Change detection for uncertain autoregressive dynamic models through nonparametric estimation 基于非参数估计的不确定自回归动态模型变化检测

Statistical Methodology Pub Date : 2016-12-01 DOI: 10.1016/j.stamet.2016.08.003

Nadine Hilgert , Ghislain Verdier , Jean-Pierre Vila

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引用次数: 4

A novel power-based approach to Gaussian kernel selection in the kernel-based association test 在基于核的关联测试中，一种新的基于幂的高斯核选择方法

Statistical Methodology Pub Date : 2016-12-01 DOI: 10.1016/j.stamet.2016.09.003

Xiang Zhan , Debashis Ghosh

引用次数: 1

A generalized inverse trinomial distribution with application 广义逆三叉分布及其应用

Statistical Methodology Pub Date : 2016-12-01 DOI: 10.1016/j.stamet.2016.10.001

Shin Zhu Sim , Seng Huat Ong

{"title":"A generalized inverse trinomial distribution with application","authors":"Shin Zhu Sim , Seng Huat Ong","doi":"10.1016/j.stamet.2016.10.001","DOIUrl":"https://doi.org/10.1016/j.stamet.2016.10.001","url":null,"abstract":"<div><p><span><span>This paper considers a particular generalized inverse trinomial distribution which may be regarded as the </span>convolution<span> of binomial and negative distributions for the statistical analysis of count data. This distribution has the flexibility to cater for under-, equi- and over-dispersion in the data. Some basic and probabilistic properties and tail approximation of the distribution have been derived. Conditions for the numerical stability of the two-term probability<span> recurrence formula have also been examined to facilitate computation. For the purpose of statistical analysis, test of hypothesis for equi-dispersion by the score and </span></span></span>likelihood ratio tests<span> and simulation study of their power, parameter estimation by maximum likelihood and a probability generating function<span> based methods have been considered. The versatility of the distribution is illustrated by its application to real biological data sets which exhibit under and over dispersion. It is shown that the distribution fits better than the well-known generalized Poisson and COM-Poisson distributions.</span></span></p></div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2016.10.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136837557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Non-parametric Bayesian inference for continuous density hidden Markov mixture model 连续密度隐马尔可夫混合模型的非参数贝叶斯推理

Statistical Methodology Pub Date : 2016-12-01 DOI: 10.1016/j.stamet.2016.10.003

Najmeh Bathaee, Hamid Sheikhzadeh

引用次数: 2

Estimation and goodness-of-fit in latent trait models: A comparison among theoretical approaches 潜在性状模型的估计和拟合优度:理论方法的比较

Statistical Methodology Pub Date : 2016-12-01 DOI: 10.1016/j.stamet.2016.05.002

Juan Carlos Bustamante , Edixon Chacón

引用次数: 1

Some new results on the Rényi quantile entropy Ordering 关于rsamnyi分位数熵排序的一些新结果

Statistical Methodology Pub Date : 2016-12-01 DOI: 10.1016/j.stamet.2016.04.003

Lei Yan , Dian-tong Kang

引用次数: 7

Forward selection and estimation in high dimensional single index models 高维单指标模型的前向选择与估计

Statistical Methodology Pub Date : 2016-12-01 DOI: 10.1016/j.stamet.2016.09.002

Shikai Luo, Subhashis Ghosal

{"title":"Forward selection and estimation in high dimensional single index models","authors":"Shikai Luo, Subhashis Ghosal","doi":"10.1016/j.stamet.2016.09.002","DOIUrl":"https://doi.org/10.1016/j.stamet.2016.09.002","url":null,"abstract":"<div><p>We propose a new variable selection and estimation technique for high dimensional single index models with unknown monotone smooth link function. Among many predictors, typically, only a small fraction of them have significant impact on prediction. In such a situation, more interpretable models with better prediction accuracy can be obtained by variable selection. In this article, we propose a new penalized forward selection technique which can reduce high dimensional optimization problems to several one dimensional optimization problems by choosing the best predictor and then iterating the selection steps until convergence. The advantage of optimizing in one dimension is that the location of optimum solution can be obtained with an intelligent search by exploiting smoothness of the criterion function. Moreover, these one dimensional optimization problems can be solved in parallel to reduce computing time nearly to the level of the one-predictor problem. Numerical comparison with the LASSO and the shrinkage sliced inverse regression shows very promising performance of our proposed method.</p></div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2016.09.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136837555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Symmetric directional false discovery rate control 对称定向错误发现率控制

Statistical Methodology Pub Date : 2016-12-01 DOI: 10.1016/j.stamet.2016.08.002

Sarah E. Holte , Eva K. Lee , Yajun Mei

{"title":"Symmetric directional false discovery rate control","authors":"Sarah E. Holte , Eva K. Lee , Yajun Mei","doi":"10.1016/j.stamet.2016.08.002","DOIUrl":"https://doi.org/10.1016/j.stamet.2016.08.002","url":null,"abstract":"<div><p><span>This research is motivated from the analysis of a real gene expression data that aims to identify a subset of “interesting” or “significant” genes for further studies. When we blindly applied the standard false discovery rate (FDR) methods, our biology collaborators were suspicious or confused, as the selected list of significant genes was highly unbalanced: there were ten times more under-expressed genes than the over-expressed genes. Their concerns led us to realize that the observed two-sample </span><span><math><mi>t</mi></math></span>-statistics were highly skewed and asymmetric, and thus the standard FDR methods might be inappropriate. To tackle this case, we propose a symmetric directional FDR control method that categorizes the genes into “over-expressed” and “under-expressed” genes, pairs “over-expressed” and “under-expressed” genes, defines the <span><math><mi>p</mi></math></span><span>-values for gene pairs via column permutations, and then applies the standard FDR method to select “significant” gene pairs instead of “significant” individual genes. We compare our proposed symmetric directional FDR method with the standard FDR method by applying them to simulated data and several well-known real data sets.</span></p></div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2016.08.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136837482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Nonparametric M-estimation for right censored regression model with stationary ergodic data 平稳遍历数据右截尾回归模型的非参数m估计

Statistical Methodology Pub Date : 2016-12-01 DOI: 10.1016/j.stamet.2016.10.002

Mohamed Chaouch , Naâmane Laïb , Elias Ould Saïd

引用次数: 7

Discrete time software reliability modeling with periodic debugging schedule 具有周期性调试计划的离散时间软件可靠性建模

Statistical Methodology Pub Date : 2016-12-01 DOI: 10.1016/j.stamet.2016.08.006

Sudipta Das, Anup Dewanji, Debasis Sengupta

引用次数: 2