Statistical Methodology最新文献_第10页

Sequential tests controlling generalized familywise error rates 控制广义家族错误率的顺序测试

Statistical Methodology Pub Date : 2015-03-01 DOI: 10.1016/j.stamet.2014.10.001

Shyamal K. De , Michael Baron

引用次数: 11

On the Chao and Zelterman estimators in a binomial mixture model 二项混合模型的Chao和Zelterman估计量

Statistical Methodology Pub Date : 2015-01-01 DOI: 10.1016/j.stamet.2014.06.002

Chang Xuan Mao, Nan Yang, Jinhua Zhong

引用次数: 0

A note on the simultaneous confidence intervals for the successive differences of exponential location parameters under heteroscedasticity 异方差条件下指数位置参数连续差异的同时置信区间注记

Statistical Methodology Pub Date : 2015-01-01 DOI: 10.1016/j.stamet.2014.06.001

Mahmood Kharrati-Kopaei

引用次数: 10

Incorporating auxiliary information for improved prediction using combination of kernel machines 结合辅助信息改进核机组合预测

Statistical Methodology Pub Date : 2015-01-01 DOI: 10.1016/j.stamet.2014.08.001

Xiang Zhan , Debashis Ghosh

{"title":"Incorporating auxiliary information for improved prediction using combination of kernel machines","authors":"Xiang Zhan , Debashis Ghosh","doi":"10.1016/j.stamet.2014.08.001","DOIUrl":"10.1016/j.stamet.2014.08.001","url":null,"abstract":"<div>With evolving genomic technologies, it is possible to get different measures of the same underlying biological phenomenon using different technologies. The goal of this paper is to build a prediction model for an outcome variable <math><mi>Y</mi></math> from covariates <math><mi>X</mi></math>. Besides <math><mi>X</mi></math>, we have surrogate covariates <math><mi>W</mi></math> which are related to <math><mi>X</mi></math>. We want to utilize the information in <math><mi>W</mi></math> to boost the prediction for <math><mi>Y</mi></math> using <math><mi>X</mi></math>. In this paper, we propose a kernel machine-based method to improve prediction of <math><mi>Y</mi></math> by <math><mi>X</mi></math> by incorporating auxiliary information <math><mi>W</mi></math>. By combining single kernel machines, we also propose a hybrid kernel machine predictor, which can yield a smaller prediction error than its constituents. The prediction error of our kernel machine predictors is evaluated using simulations. We also apply our method to a lung cancer dataset and an Alzheimer’s disease dataset.</div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2014.08.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32835018","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: 4

Maximum entropy test for GARCH models GARCH模型的最大熵检验

Statistical Methodology Pub Date : 2015-01-01 DOI: 10.1016/j.stamet.2014.05.002

Jiyeon Lee , Sangyeol Lee , Siyun Park

引用次数: 13

Confidence distributions: A review 信心分布:综述

Statistical Methodology Pub Date : 2015-01-01 DOI: 10.1016/j.stamet.2014.07.002

Saralees Nadarajah , Sergey Bityukov , Nikolai Krasnikov

引用次数: 23

Modified SCAD penalty for constrained variable selection problems 改进了约束变量选择问题的SCAD惩罚

Statistical Methodology Pub Date : 2014-11-01 DOI: 10.1016/j.stamet.2014.05.001

Chi Tim Ng , Chi Wai Yu

引用次数: 0

General tests of independence based on empirical processes indexed by functions 基于函数索引的经验过程的一般独立性检验

Statistical Methodology Pub Date : 2014-11-01 DOI: 10.1016/j.stamet.2014.03.001

Salim Bouzebda

引用次数: 9

Bayesian inference in nonparametric dynamic state-space models 非参数动态状态空间模型中的贝叶斯推理

Statistical Methodology Pub Date : 2014-11-01 DOI: 10.1016/j.stamet.2014.02.004

Anurag Ghosh , Soumalya Mukhopadhyay , Sandipan Roy , Sourabh Bhattacharya

{"title":"Bayesian inference in nonparametric dynamic state-space models","authors":"Anurag Ghosh , Soumalya Mukhopadhyay , Sandipan Roy , Sourabh Bhattacharya","doi":"10.1016/j.stamet.2014.02.004","DOIUrl":"10.1016/j.stamet.2014.02.004","url":null,"abstract":"<div>We introduce state-space models where the functionals of the observational and evolutionary equations are unknown, and treated as random functions evolving with time. Thus, our model is nonparametric and generalizes the traditional parametric state-space models. This random function approach also frees us from the restrictive assumption that the functional forms, although time-dependent, are of fixed forms. The traditional approach of assuming known, parametric functional forms is questionable, particularly in state-space models, since the validation of the assumptions require data on both the observed time series and the latent states; however, data on the latter are not available in state-space models.We specify Gaussian processes as priors of the random functions and exploit the “look-up table approach” of Bhattacharya (2007) to efficiently handle the dynamic structure of the model. We consider both univariate and multivariate situations, using the Markov chain Monte Carlo (MCMC) approach for studying the posterior distributions of interest. We illustrate our methods with simulated data sets, in both univariate and multivariate situations. Moreover, using our Gaussian process approach we analyze a real data set, which has also been analyzed by Shumway & Stoffer (1982) and Carlin, Polson & Stoffer (1992) using the linearity assumption. Interestingly, our analyses indicate that towards the end of the time series, the linearity assumption is perhaps questionable.</div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2014.02.004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"55092292","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: 17

Multiple crossing sequential fixed-size confidence region methodologies for a multivariate normal mean vector 多元正态均值向量的多交叉序贯固定大小置信区域方法

Statistical Methodology Pub Date : 2014-11-01 DOI: 10.1016/j.stamet.2014.03.003

Nitis Mukhopadhyay, Sankha Muthu Poruthotage

{"title":"Multiple crossing sequential fixed-size confidence region methodologies for a multivariate normal mean vector","authors":"Nitis Mukhopadhyay, Sankha Muthu Poruthotage","doi":"10.1016/j.stamet.2014.03.003","DOIUrl":"10.1016/j.stamet.2014.03.003","url":null,"abstract":"<div>The asymptotically efficient and asymptotically consistent purely sequential procedure of Mukhopadhyay and Al-Mousawi (1986) is customarily used to construct a confidence region <math><mi>R</mi></math> for the mean vector <math><mstyle><mi>μ</mi></mstyle></math> of <math><msub><mrow><mi>N</mi></mrow><mrow><mi>p</mi></mrow></msub><mrow><mo>(</mo><mstyle><mi>μ</mi></mstyle><mo>,</mo><msup><mrow><mi>σ</mi></mrow><mrow><mn>2</mn></mrow></msup><mstyle><mi>H</mi></mstyle><mo>)</mo></mrow></math>. This procedure does not have the exact consistency property. <math><msub><mrow><mstyle><mi>H</mi></mstyle></mrow><mrow><mi>p</mi><mo>×</mo><mi>p</mi></mrow></msub></math> is assumed known and positive definite with <math><msup><mrow><mi>σ</mi></mrow><mrow><mn>2</mn></mrow></msup></math> unknown. The maximum diameter of <math><mi>R</mi></math> and the confidence coefficient are prefixed.A purely sequential sampling strategy is proposed allowing sampling until sample size crosses the boundary multiple times. We ascertain asymptotic efficiency and asymptotic consistency properties (Theorem 3.1). Its ability to nearly achieve required coverage probability without significant over-sampling is demonstrated with simulations. A truncation technique plus fine-tuning of the multiple crossing rule are proposed to increase practicality. Two real data illustrations are highlighted.</div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2014.03.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"55092338","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: 2