Statistica Neerlandica最新文献_第7页

Average ordinary least squares‐centered penalized regression: A more efficient way to address multicollinearity than ridge regression 平均普通最小二乘中心惩罚回归:一种比脊回归更有效的解决多重共线性的方法

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2022-01-23 DOI: 10.1111/stan.12263

Wei Wang, Linjiang Li, Sheng Li, F. Yin, Fang Liao, Zhang Tao, Xiaosong Li, Xiong Xiao, Yue Ma

{"title":"Average ordinary least squares‐centered penalized regression: A more efficient way to address multicollinearity than ridge regression","authors":"Wei Wang, Linjiang Li, Sheng Li, F. Yin, Fang Liao, Zhang Tao, Xiaosong Li, Xiong Xiao, Yue Ma","doi":"10.1111/stan.12263","DOIUrl":"https://doi.org/10.1111/stan.12263","url":null,"abstract":"We developed a novel method to address multicollinearity in linear models called average ordinary least squares (OLS)‐centered penalized regression (AOPR). AOPR penalizes the cost function to shrink the estimators toward the weighted‐average OLS estimator. The commonly used ridge regression (RR) shrinks the estimators toward zero, that is, employs penalization prior β∼N(0,1/k) in the Bayesian view, which contradicts the common real prior β≠0 . Therefore, RR selects small penalization coefficients to relieve such a contradiction and thus makes the penalizations inadequate. Mathematical derivations remind us that AOPR could increase the performance of RR and OLS regression. A simulation study shows that AOPR obtains more accurate estimators than OLS regression in most situations and more accurate estimators than RR when the signs of the true β s are identical and is slightly less accurate than RR when the signs of the true β s are different. Additionally, a case study shows that AOPR obtains more stable estimators and stronger statistical power and predictive ability than RR and OLS regression. Through these results, we recommend using AOPR to address multicollinearity more efficiently than RR and OLS regression, especially when the true β s have identical signs.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"88 1","pages":"347 - 368"},"PeriodicalIF":1.5,"publicationDate":"2022-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81131506","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}

引用次数: 0

Prediction intervals for all of M future observations based on linear random effects models 基于线性随机效应模型的所有M个未来观测值的预测区间

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2021-12-19 DOI: 10.1111/stan.12260

M. Menssen, F. Schaarschmidt

{"title":"Prediction intervals for all of M future observations based on linear random effects models","authors":"M. Menssen, F. Schaarschmidt","doi":"10.1111/stan.12260","DOIUrl":"https://doi.org/10.1111/stan.12260","url":null,"abstract":"In many pharmaceutical and biomedical applications such as assay validation, assessment of historical control data, or the detection of anti‐drug antibodies, the calculation and interpretation of prediction intervals (PI) is of interest. The present study provides two novel methods for the calculation of prediction intervals based on linear random effects models and restricted maximum likelihood (REML) estimation. Unlike other REML‐based PI found in the literature, both intervals reflect the uncertainty related with the estimation of the prediction variance. The first PI is based on Satterthwaite approximation. For the other PI, a bootstrap calibration approach that we will call quantile‐calibration was used. Due to the calibration process this PI can be easily computed for more than one future observation and based on balanced and unbalanced data as well. In order to compare the coverage probabilities of the proposed PI with those of four intervals found in the literature, Monte Carlo simulations were run for two relatively complex random effects models and a broad range of parameter settings. The quantile‐calibrated PI was implemented in the statistical software R and is available in the predint package.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"66 1","pages":"283 - 308"},"PeriodicalIF":1.5,"publicationDate":"2021-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77968796","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}

引用次数: 3

Bayesian model selection for multilevel mediation models 多层次中介模型的贝叶斯模型选择

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2021-09-29 DOI: 10.1111/stan.12256

O. Ariyo, E. Lesaffre, G. Verbeke, M. Huisman, Judith Rijnhart, Martijn Heymans, J. Twisk

引用次数: 2

Competing risks regression for clustered survival data via the marginal additive subdistribution hazards model 基于边际加性亚分布风险模型的聚类生存数据竞争风险回归

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2021-09-13 DOI: 10.1111/stan.12317

Xinyuan Chen, D. Esserman, Fan Li

{"title":"Competing risks regression for clustered survival data via the marginal additive subdistribution hazards model","authors":"Xinyuan Chen, D. Esserman, Fan Li","doi":"10.1111/stan.12317","DOIUrl":"https://doi.org/10.1111/stan.12317","url":null,"abstract":"A population‐averaged additive subdistribution hazards model is proposed to assess the marginal effects of covariates on the cumulative incidence function and to analyze correlated failure time data subject to competing risks. This approach extends the population‐averaged additive hazards model by accommodating potentially dependent censoring due to competing events other than the event of interest. Assuming an independent working correlation structure, an estimating equations approach is outlined to estimate the regression coefficients and a new sandwich variance estimator is proposed. The proposed sandwich variance estimator accounts for both the correlations between failure times and between the censoring times, and is robust to misspecification of the unknown dependency structure within each cluster. We further develop goodness‐of‐fit tests to assess the adequacy of the additive structure of the subdistribution hazards for the overall model and each covariate. Simulation studies are conducted to investigate the performance of the proposed methods in finite samples. We illustrate our methods using data from the STrategies to Reduce Injuries and Develop confidence in Elders (STRIDE) trial.This article is protected by copyright. All rights reserved.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"58 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90297672","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}

引用次数: 3

Joint probabilities under expected value constraints, transportation problems, maximum entropy in the mean 期望值约束下的联合概率，运输问题，最大平均熵

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2021-09-02 DOI: 10.1111/stan.12314

H. Gzyl, Silvia Mayoral

{"title":"Joint probabilities under expected value constraints, transportation problems, maximum entropy in the mean","authors":"H. Gzyl, Silvia Mayoral","doi":"10.1111/stan.12314","DOIUrl":"https://doi.org/10.1111/stan.12314","url":null,"abstract":"There are interesting extensions of the problem of determining a joint probability with known marginals. On the one hand, one may impose size constraints on the joint probabilities. On the other, one may impose additional constraints like the expected values of known random variables. If we think of the marginal probabilities as demands or supplies, and of the joint probability as the fraction of the supplies to be shipped from the production sites to the demand sites, instead of joint probabilities we can think of transportation policies. Clearly, fixing the cost of a transportation policy is equivalent to an integral constraints upon the joint probability. We will show how to solve the cost constrained transportation problem by means of the method of maximum entropy in the mean. We shall also show how this approach leads to an interior point like method to solve the associated linear programming problem. We shall also investigate some geometric structure the space of transportation policies, or joint probabilities or pixel space, using a Riemannian structure associated with the dual of the entropy used to determine bounds between probabilities or between transportation policies.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81947429","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}

引用次数: 0

Logistic or not Logistic? 物流还是不物流?

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2021-08-16 DOI: 10.1111/stan.12292

J. Allison, B. Ebner, M. Smuts

引用次数: 1

Bootstrap for integer‐valued GARCH(p, q) processes 整数值GARCH(p, q)过程的自举

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2021-08-01 DOI: 10.1111/stan.12238

M. Neumann

引用次数: 4

Goodness‐of‐fit tests for Poisson count time series based on the Stein–Chen identity 基于Stein-Chen恒等式的泊松计数时间序列的拟合优度检验