Statistical Methodology最新文献_第9页

Shrinkage, pretest, and penalty estimators in generalized linear models 广义线性模型中的收缩、预试和惩罚估计

Statistical Methodology Pub Date : 2015-05-01 DOI: 10.1016/j.stamet.2014.11.003

Shakhawat Hossain , S. Ejaz Ahmed , Kjell A. Doksum

{"title":"Shrinkage, pretest, and penalty estimators in generalized linear models","authors":"Shakhawat Hossain , S. Ejaz Ahmed , Kjell A. Doksum","doi":"10.1016/j.stamet.2014.11.003","DOIUrl":"10.1016/j.stamet.2014.11.003","url":null,"abstract":"<div>We consider estimation in generalized linear models when there are many potential predictors and some of them may not have influence on the response of interest. In the context of two competing models where one model includes all predictors and the other restricts variable coefficients to a candidate linear subspace based on subject matter or prior knowledge, we investigate the relative performances of Stein type shrinkage, pretest, and penalty estimators (<math><msub><mrow><mi>L</mi></mrow><mrow><mn>1</mn></mrow></msub></math>GLM, adaptive <math><msub><mrow><mi>L</mi></mrow><mrow><mn>1</mn></mrow></msub></math>GLM, and SCAD) with respect to the unrestricted maximum likelihood estimator (MLE). The asymptotic properties of the pretest and shrinkage estimators including the derivation of asymptotic distributional biases and risks are established. In particular, we give conditions under which the shrinkage estimators are asymptotically more efficient than the unrestricted MLE. A Monte Carlo simulation study shows that the mean squared error (MSE) of an adaptive shrinkage estimator is comparable to the MSE of the penalty estimators in many situations and in particular performs better than the penalty estimators when the dimension of the restricted parameter space is large. The Steinian shrinkage and penalty estimators all improve substantially on the unrestricted MLE. A real data set analysis is also presented to compare the suggested methods.</div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2014.11.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"55092619","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

Some properties of stochastic volatility model that are induced by its volatility sequence 随机波动模型的一些性质是由波动序列引起的

Statistical Methodology Pub Date : 2015-05-01 DOI: 10.1016/j.stamet.2014.11.002

M. Rezapour , N. Balakrishnan

引用次数: 0

Different methods for handling incomplete longitudinal binary outcome due to missing at random dropout 不同的方法处理不完整的纵向二进制结果由于缺失在随机辍学

Statistical Methodology Pub Date : 2015-05-01 DOI: 10.1016/j.stamet.2014.10.002

A. Satty , H. Mwambi , G. Molenberghs

{"title":"Different methods for handling incomplete longitudinal binary outcome due to missing at random dropout","authors":"A. Satty , H. Mwambi , G. Molenberghs","doi":"10.1016/j.stamet.2014.10.002","DOIUrl":"10.1016/j.stamet.2014.10.002","url":null,"abstract":"<div>This paper compares the performance of weighted generalized estimating equations (WGEEs), multiple imputation based on generalized estimating equations (MI-GEEs) and generalized linear mixed models (GLMMs) for analyzing incomplete longitudinal binary data when the underlying study is subject to dropout. The paper aims to explore the performance of the above methods in terms of handling dropouts that are missing at random (MAR). The methods are compared on simulated data. The longitudinal binary data are generated from a logistic regression model, under different sample sizes. The incomplete data are created for three different dropout rates. The methods are evaluated in terms of bias, precision and mean square error in case where data are subject to MAR dropout. In conclusion, across the simulations performed, the MI-GEE method performed better in both small and large sample sizes. Evidently, this should not be seen as formal and definitive proof, but adds to the body of knowledge about the methods’ relative performance. In addition, the methods are compared using data from a randomized clinical trial.</div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2014.10.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"55092566","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: 9

Estimation in step-stress life tests with complementary risks from the exponentiated exponential distribution under time constraint and its applications to UAV data 基于时间约束下指数分布的互补风险阶跃应力寿命试验估计及其在无人机数据中的应用

Statistical Methodology Pub Date : 2015-03-01 DOI: 10.1016/j.stamet.2014.09.001

David Han

{"title":"Estimation in step-stress life tests with complementary risks from the exponentiated exponential distribution under time constraint and its applications to UAV data","authors":"David Han","doi":"10.1016/j.stamet.2014.09.001","DOIUrl":"10.1016/j.stamet.2014.09.001","url":null,"abstract":"<div>In accelerated step-stress life tests, the stress levels are allowed to increase at some pre-determined time points such that information on the lifetime parameters can be obtained more quickly than under normal operating conditions. Because there are often multiple causes for the failure of a test unit, such as mechanical or electrical failures, in this article, a step-stress model under time constraint is studied when the lifetimes of different complementary risk factors are independent from exponentiated distributions. Although the baseline distributions can belong to a general class of distributions, including Weibull, Pareto, and Gompertz distributions, particular attention is paid to the case of an exponentiated exponential distribution. Under this setup, the maximum likelihood estimators of the unknown scale and shape parameters of the different causes are derived with the assumption of cumulative damage. Using the asymptotic distributions and the parametric bootstrap method, the confidence intervals for the parameters are then constructed. The precision of the estimates and the performance of the confidence intervals are also assessed through extensive Monte Carlo simulations, and finally, the inference methods discussed here are illustrated with motivating examples.</div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2014.09.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"55092463","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: 11

Extending a double sampling control chart for non-conforming proportion in high quality processes to the case of small samples 将高质量过程中不合格率的双抽样控制图扩展到小样本情况

Statistical Methodology Pub Date : 2015-03-01 DOI: 10.1016/j.stamet.2014.09.003

Silvia Joekes , Marcelo Smrekar , Emanuel Pimentel Barbosa

{"title":"Extending a double sampling control chart for non-conforming proportion in high quality processes to the case of small samples","authors":"Silvia Joekes , Marcelo Smrekar , Emanuel Pimentel Barbosa","doi":"10.1016/j.stamet.2014.09.003","DOIUrl":"10.1016/j.stamet.2014.09.003","url":null,"abstract":"<div>When production processes reach high quality standards they are known as high quality processes. In this situation, the conventional <math><mi>p</mi></math> charts (based on 3-sigma limits) used for monitoring non-conforming products have serious drawbacks in detecting changes in <math><mi>p</mi></math> due to excess of false alarm risk. In a previous paper, the authors showed a new <math><mi>p</mi></math> chart that provides a large improvement over the usual <math><mi>p</mi></math> chart in these situations. In this paper, authors propose a new corrected version of a double sampling (DS) control chart for monitoring the proportion <math><mi>p</mi></math> of non-conforming presented in the literature for large samples, in order to extend its applicability to the case of small samples. This procedure offers better statistical efficiency (in terms of the average run length) than the previous <math><mi>p</mi></math> charts, without increasing the sampling. Tables are provided to aid in the choice of DS parameters. The benefits of the corrected version of a DS chart for monitoring high-quality processes are illustrated with real data.</div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2014.09.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"55092488","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: 12

Semiparametric empirical likelihood tests in varying coefficient partially linear models with repeated measurements 重复测量的变系数部分线性模型的半参数经验似然检验

Statistical Methodology Pub Date : 2015-03-01 DOI: 10.1016/j.stamet.2014.10.003

Peixin Zhao , Yiping Yang

引用次数: 3

Exact likelihood inference for an exponential parameter under generalized progressive hybrid censoring scheme 广义渐进式混合滤波方案下指数参数的精确似然推断

Statistical Methodology Pub Date : 2015-03-01 DOI: 10.1016/j.stamet.2014.09.002

Youngseuk Cho, Hokeun Sun, Kyeongjun Lee

引用次数: 83

An approximation of logarithmic functions in the regression setting 在回归设置中对对数函数的近似

Statistical Methodology Pub Date : 2015-03-01 DOI: 10.1016/j.stamet.2014.09.004

Tao Chen , Kenneth A. Couch

引用次数: 2

Maximum likelihood estimators for extended growth curve model with orthogonal between-individual design matrices 具有正交设计矩阵的扩展生长曲线模型的极大似然估计

Statistical Methodology Pub Date : 2015-03-01 DOI: 10.1016/j.stamet.2014.09.005

Daniel Klein, Ivan Žežula

引用次数: 5

On bivariate and a mixture of bivariate Birnbaum–Saunders distributions 关于二元和二元混合Birnbaum-Saunders分布

Statistical Methodology Pub Date : 2015-03-01 DOI: 10.1016/j.stamet.2014.07.001

Mohsen Khosravi , Debasis Kundu , Ahad Jamalizadeh

引用次数: 18