Statistica Neerlandica最新文献_第8页

Inverse‐probability‐weighted logrank test for stratified survival data with missing measurements 缺失测量的分层生存数据的逆概率加权logrank检验

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2022-07-21 DOI: 10.1111/stan.12276

Rim Ben Elouefi, Foued Saâdaoui

引用次数: 0

Assessing replicability with the sceptical p$$ p $$ ‐value: Type‐I error control and sample size planning 用怀疑p $$ p $$值评估可复制性:I型误差控制和样本量计划

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2022-07-01 DOI: 10.1111/stan.12312

Charlotte Micheloud, F. Balabdaoui, L. Held

引用次数: 3

Automatic bias correction for testing in high‐dimensional linear models 用于高维线性模型测试的自动偏差校正

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2022-07-01 DOI: 10.1111/stan.12274

Jing Zhou, G. Claeskens

引用次数: 0

Assessing skewness in financial markets 评估金融市场的偏差

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2022-05-30 DOI: 10.1111/stan.12273

Giovanni Campisi, L. La Rocca, S. Muzzioli

引用次数: 0

Autoregressive and moving average models for zero‐inflated count time series 零膨胀计数时间序列的自回归和移动平均模型

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2022-05-01 DOI: 10.1111/stan.12255

Vurukonda Sathish, S. Mukhopadhyay, R. Tiwari

{"title":"Autoregressive and moving average models for zero‐inflated count time series","authors":"Vurukonda Sathish, S. Mukhopadhyay, R. Tiwari","doi":"10.1111/stan.12255","DOIUrl":"https://doi.org/10.1111/stan.12255","url":null,"abstract":"Zero inflation is a common nuisance while monitoring disease progression over time. This article proposes a new observation‐driven model for zero‐inflated and over‐dispersed count time series. The counts given from the past history of the process and available information on covariates are assumed to be distributed as a mixture of a Poisson distribution and a distribution degenerated at zero, with a time‐dependent mixing probability, πt . Since, count data usually suffers from overdispersion, a Gamma distribution is used to model the excess variation, resulting in a zero‐inflated negative binomial regression model with mean parameter λt . Linear predictors with autoregressive and moving average (ARMA) type terms, covariates, seasonality and trend are fitted to λt and πt through canonical link generalized linear models. Estimation is done using maximum likelihood aided by iterative algorithms, such as Newton‐Raphson (NR) and Expectation and Maximization. Theoretical results on the consistency and asymptotic normality of the estimators are given. The proposed model is illustrated using in‐depth simulation studies and two disease datasets.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"36 1 1","pages":"190 - 218"},"PeriodicalIF":1.5,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79903003","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: 1

Threshold estimation for continuous three‐phase polynomial regression models with constant mean in the middle regime 中区均值为常数的连续三相多项式回归模型的阈值估计

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2022-04-21 DOI: 10.1111/stan.12268

Chih‐Hao Chang, Kam-Fai Wong, Wei‐Yee Lim

{"title":"Threshold estimation for continuous three‐phase polynomial regression models with constant mean in the middle regime","authors":"Chih‐Hao Chang, Kam-Fai Wong, Wei‐Yee Lim","doi":"10.1111/stan.12268","DOIUrl":"https://doi.org/10.1111/stan.12268","url":null,"abstract":"This paper considers a continuous three‐phase polynomial regression model with two threshold points for dependent data with heteroscedasticity. We assume the model is polynomial of order zero in the middle regime, and is polynomial of higher orders elsewhere. We denote this model by ℳ2$$ {mathcal{M}}_2 $$ , which includes models with one or no threshold points, denoted by ℳ1$$ {mathcal{M}}_1 $$ and ℳ0$$ {mathcal{M}}_0 $$ , respectively, as special cases. We provide an ordered iterative least squares (OiLS) method when estimating ℳ2$$ {mathcal{M}}_2 $$ and establish the consistency of the OiLS estimators under mild conditions. When the underlying model is ℳ1$$ {mathcal{M}}_1 $$ and is (d0−1)$$ left({d}_0-1right) $$ th‐order differentiable but not d0$$ {d}_0 $$ th‐order differentiable at the threshold point, we further show the Op(N−1/(d0+2))$$ {O}_pleft({N}^{-1/left({d}_0+2right)}right) $$ convergence rate of the OiLS estimators, which can be faster than the Op(N−1/(2d0))$$ {O}_pleft({N}^{-1/left(2{d}_0right)}right) $$ convergence rate given in Feder when d0≥3$$ {d}_0ge 3 $$ . We also apply a model‐selection procedure for selecting ℳκ$$ {mathcal{M}}_{kappa } $$ ; κ=0,1,2$$ kappa =0,1,2 $$ . When the underlying model exists, we establish the selection consistency under the aforementioned conditions. Finally, we conduct simulation experiments to demonstrate the finite‐sample performance of our asymptotic results.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"13 1","pages":"4 - 47"},"PeriodicalIF":1.5,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87649922","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

Optimal subsampling for multiplicative regression with massive data 海量数据乘法回归的最优子抽样

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2022-03-12 DOI: 10.1111/stan.12266

Tianzhen Wang, Haixiang Zhang

引用次数: 6

Change-point analysis through integer-valued autoregressive process with application to some COVID-19 data. 整数值自回归过程的变点分析与部分COVID-19数据的应用

IF 1.5 3区数学

Statistica Neerlandica Pub Date : 2022-02-01 Epub Date: 2021-07-11 DOI: 10.1111/stan.12251

Subhankar Chattopadhyay, Raju Maiti, Samarjit Das, Atanu Biswas

{"title":"Change-point analysis through integer-valued autoregressive process with application to some COVID-19 data.","authors":"Subhankar Chattopadhyay, Raju Maiti, Samarjit Das, Atanu Biswas","doi":"10.1111/stan.12251","DOIUrl":"https://doi.org/10.1111/stan.12251","url":null,"abstract":"In this article, we consider the problem of change-point analysis for the count time series data through an integer-valued autoregressive process of order 1 (INAR(1)) with time-varying covariates. These types of features we observe in many real-life scenarios especially in the COVID-19 data sets, where the number of active cases over time starts falling and then again increases. In order to capture those features, we use Poisson INAR(1) process with a time-varying smoothing covariate. By using such model, we can model both the components in the active cases at time-point t namely, (i) number of nonrecovery cases from the previous time-point and (ii) number of new cases at time-point t. We study some theoretical properties of the proposed model along with forecasting. Some simulation studies are performed to study the effectiveness of the proposed method. Finally, we analyze two COVID-19 data sets and compare our proposed model with another PINAR(1) process which has time-varying covariate but no change-point, to demonstrate the overall performance of our proposed model.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"76 1","pages":"4-34"},"PeriodicalIF":1.5,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/stan.12251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39154751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

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