Journal of statistical research最新文献

{"title":"Constrained inference in scaled mixed effects models with applications to London Junior School Project data","authors":"Md Mostak Ahammed, Bhaskar Bhattacharya","doi":"10.3329/jsr.v57i12.72970","DOIUrl":"https://doi.org/10.3329/jsr.v57i12.72970","url":null,"abstract":"\u0000\u0000\u0000Multiple outcomes arise frequently in many different settings. Mixed effect models are a useful tool for the analyses of such data. However, when outcomes are measured on different scales, analyses based on any one scale are misleading. Often parameters of the model are subject to known order restrictions. To incorporate heterogeneity across different outcomes, we propose a scaled linear mixed effect model. To estimate parameters, we propose a maximum likelihood estimation procedure based on a restricted version of the expectation-conditional maximization either algorithm. Constrained hypotheses testing procedures are developed using likelihood ratio tests. The empirical significance levels and powers are studied using simulations. This article shows that incorporating the restrictions improves the mean squared errors of the estimates and the power of the tests. The methodology is applied on the London Junior School Project data incorporating known restrictions of patterns of scores.\u0000Journal of Statistical Research 2023, Vol 57, No.1-2, pp.81-94\u0000\u0000\u0000","PeriodicalId":90001,"journal":{"name":"Journal of statistical research","volume":"112 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140986845","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}

{"title":"Modeling of ordinal longitudinal accelerometer data","authors":"Drew Lazar, Fatimah Alawad, M. Begum, Alexander H K Montoye","doi":"10.3329/jsr.v57i12.72965","DOIUrl":"https://doi.org/10.3329/jsr.v57i12.72965","url":null,"abstract":"\u0000\u0000\u0000Improvements in accelerometer technology has led to new types of data on which more powerful predictive models can be built to assess physical activity. This paper implements an ordinal random forest model with recursive forecasting to take into account the ordinal, longitudinal nature of responses. The data comes from 28 adults performing activities of daily living in two visits, while wearing accelerometers on the ankle, hip, right and left wrist. The first visit provided training data and the second testing data so that an indepen- dent sample, cross-validation approach could be used. For this type of data, prior responses are not available at the testing stage or in practice. However, recursive forecasts can be made with prior predictions in place of lagging responses on models which were built to use lagging responses as explanatory variables. Models are fit to account for multiple time series, with different time series for each participant in the study. We found that ordinal random forest, when the time series is taken into account, produces better accuracy rates and better linearly weighted kappa values than both ordinary ordinal forest and random forest. On the testing set, the lowest error rates were produced by the ankle (28.0%), followed by the left wrist (28.7%), hip (28.9%) and then the right wrist (30.2%) using the best performing decision model for a four-activity level response. In addition, linearly weighted kappa values indicated substantial agreement. The approach of this work can be adapted to other types of longitudinal ordinal models for improvements in modeling techniques.\u0000Journal of Statistical Research 2022, Vol. 57, No. 1-2, pp.1-19\u0000\u0000\u0000","PeriodicalId":90001,"journal":{"name":"Journal of statistical research","volume":"106 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140986970","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}

{"title":"Confidence intervals for mean response times of a open queueing network: calibration approach","authors":"S. Pathare","doi":"10.3329/jsr.v57i12.72969","DOIUrl":"https://doi.org/10.3329/jsr.v57i12.72969","url":null,"abstract":"\u0000 \u0000 \u0000Response time is a key factor in the analysis of the different queueing network model properties. This study computes a series of response times using a data-based recurrence relation. The true average response time is determined using the sample averages from those response times. Several confidence intervals are created for the open queueing net- work model’s average response times using the calibration approach. Using a numerical simulation analysis, the accuracy of the various confidence intervals is evaluated. \u0000Journal of Statistical Research 2023, Vol 57, No.1-2, pp.69-79 \u0000 \u0000 \u0000","PeriodicalId":90001,"journal":{"name":"Journal of statistical research","volume":"105 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140986788","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}

{"title":"A simulation study to assess the impact missing values on the performance of different statistical methods for analysis of binary repeated measures data with an additional hierarchical structure","authors":"E. Masaoud, Henrik Stryhn","doi":"10.3329/jsr.v57i12.72968","DOIUrl":"https://doi.org/10.3329/jsr.v57i12.72968","url":null,"abstract":"\u0000 \u0000 \u0000The primary objective of the study was to assess the impact of missing values on the analy- sis of binary repeated measures data with an additional hierarchical structure. One motivat- ing example for the present study was records of high somatic cell counts in milk samples obtained by approximately monthly sampling throughout the lactations of cows in dairy herds. Random effects models with autocorrelated (ρ = 1, 0.9 or 0.5) subject-level ran- dom effects were behind the simulated data. In general, the settings of the simulation were chosen to reflect a real somatic cell count dataset (scc40), except that the within-cow time series length was set to 8-time points for each cow. The estimation procedures consid- ered were: Ordinary Logistic Regression (OLR), Alternating Logistic Regression (ALR), Weighted Generalized Estimating Equations (WGEE), Penalized Quasi Likelihood (PQL), Maximum likelihood via numerical integration (ML) and Bayesian Markov chain Monte Carlo (MCMC). Multiple scenarios of simulated incomplete datasets were considered and include: a scenario corresponded to a combination of missingness patterns present in the scc40 dataset (scc40 scenario) The remaining scenarios involved only drop-outs, and corre- sponded to either moderate or high percentages of values either missing at random (MAR) or not missing at random (NMAR), respectively. In the scc40 scenario, all estimation procedures except OLR performed well and produced estimates with small relative bias (generally less than 5%) for levels of missingness that roughly corresponded to the scc40 data. In MAR missingness scenarios, some biases were found for ALR, WGEE and PQL procedures, whereas the likelihood-based procedures were largely unaffected by the miss- ing values. In NMAR scenarios, all procedures experienced similar and strong biases in the time coefficient; however, fixed effects estimates at the subject and cluster levels were relatively unaffected. \u0000Journal of Statistical Research 2023, Vol 57, No.1-2, pp.35-67 \u0000 \u0000 \u0000","PeriodicalId":90001,"journal":{"name":"Journal of statistical research","volume":"109 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140986757","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}

{"title":"Incorporating treatment-covariate interaction in designing a covariate adjusted response adaptive allocation for binary response trials","authors":"Rahul Bhattacharya, A. Biswas","doi":"10.3329/jsr.v57i12.72967","DOIUrl":"https://doi.org/10.3329/jsr.v57i12.72967","url":null,"abstract":"\u0000\u0000\u0000Assuming binary treatment outcomes, a covariate adjusted response adaptive (CARA) design is developed for multi-arm clinical trials assuming the presence of treatment-covariate interaction. The proposed allocation skews the allocation dynamically according to the performance of the treatments and the covariate profile of any incoming subject. Relevant de- sign and precision based measures of the performance are further investigated empirically, which established the usefulness of the proposed allocation design over its competitors.\u0000Journal of Statistical Research 2022, Vol. 57, No. 1-2, pp.21-33\u0000\u0000\u0000","PeriodicalId":90001,"journal":{"name":"Journal of statistical research","volume":"106 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140987103","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}

{"title":"Debias random forest regression predictors","authors":"Lihua Chen, Prabhashi W. Withana Gamage, J. Ryan","doi":"10.3329/jsr.v56i2.67466","DOIUrl":"https://doi.org/10.3329/jsr.v56i2.67466","url":null,"abstract":"\u0000 \u0000 \u0000The random forest can reduce the variance of regression predictors through bagging while leaving the bias mostly unchanged. In general, the bias is not negligible and consequently bias correction is necessary. The default bias correction method implemented in the R package randomForest often works poorly. Several approaches have been developed which in general outperform the R default. However, little work has been done to com- prehensively evaluate the performance of these methods and thus guide users to select an appropriate method for bias correction. This paper fills this gap by providing an informa- tive ranking of these bias correction methods based on an extensive numerical study. We further offered practical suggestions on the application of the winner of these methods and suggested a visualization technique to help users decide when bias correction is needed. \u0000Journal of Statistical Research 2022, Vol. 56, No. 2, pp. 115-131 \u0000 \u0000 \u0000","PeriodicalId":90001,"journal":{"name":"Journal of statistical research","volume":"36 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41257181","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}

{"title":"Penalized logistic normal multinomial factor analyzers for high dimensional compositional data","authors":"Wangshu Tu, Sanjeena Subedi","doi":"10.3329/jsr.v56i2.67469","DOIUrl":"https://doi.org/10.3329/jsr.v56i2.67469","url":null,"abstract":"\u0000 \u0000 \u0000Model-based clustering utilizes a finite mixture model to identify underlying patterns or clusters across samples. A finite mixture model is a convex combination of two or more distributions, where appropriate distributions are chosen depending on the type of the data. Recently, there has been a great interest in clustering human microbiome data. Microbiome data are compositional (yielding relative abundance) and are high-dimensional. Previously, a family of logistic normal multinomial factor analyzers (LNM-FA) for model-based clus- tering of high-dimensional microbiome data was proposed via a factor analyzer structure. This reduced the number of parameters and computation overhead compared to a traditional mixtures of logistic normal multinomial models. Here, we propose a penalized LNM-FA (PLNM-FA) model by utilizing lasso regularization to each entry of the loading matrix. This introduces further parsimony compared to LNM-FA and also estimates the number of latent factors simultaneously. Parameter estimation is done using a variational variant of the alternating expectation conditional maximization algorithm to maximize the penalized maximum likelihood. The performance of proposed algorithm is evaluated using simula- tion studies and real data. \u0000Journal of Statistical Research 2022, Vol. 56, No. 2, pp.185-216  \u0000 \u0000 \u0000","PeriodicalId":90001,"journal":{"name":"Journal of statistical research","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48828835","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}

{"title":"Approximate Methods For Analyzing Semi-Parametric Longitudinal Models With Non-Ignorable Missing Responses","authors":"Najla Aloraini","doi":"10.22215/etd/2022-15135","DOIUrl":"https://doi.org/10.22215/etd/2022-15135","url":null,"abstract":"We often encounter missing data in longitudinal studies. When the missingness in longitudinal data is nonignorable, it is necessary to incorporate the missing data mechanism into the observed data likelihood function for a valid statistical inference. In this article, we propose and explore a novel semiparametric approach to estimating the regression parameters and variance components using a partially linear mixed model with nonignorable and nonmonotone missing responses. The finite sample properties of the proposed method are studied using Monte Carlo simulations, where our method is found to be very effective in capturing any curvilinear pattern in the mean response. The method is also illustrated using some actual longitudinal data obtained from a public health survey, referred to as the Health and Retirement Study (HRS). \u0000Journal of Statistical Research 2022, Vol. 56, No. 2, pp. 155-183","PeriodicalId":90001,"journal":{"name":"Journal of statistical research","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45040120","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}

{"title":"Properties of inverse probability of adherence weighted estimator of the per-protocol effect for sustained treatment strategies under different data-generating mechanisms and adherence patterns","authors":"L. Mosquera, Mohammad Ehsanul Karim, M. B. Hossain","doi":"10.3329/jsr.v56i2.67467","DOIUrl":"https://doi.org/10.3329/jsr.v56i2.67467","url":null,"abstract":"\u0000 \u0000 \u0000Inverse Probability (of Adherence) Weighted per-protocol (IPW-PP) estimators are get- ting popular in addressing medication non-adherence while analyzing pragmatic trial data. However, their finite sample properties under different data generating mechanisms (DGMs) have not been investigated comprehensively. In the current work, we investigated the finite sample performances of such estimators in the context of a pragmatic random- ized controlled trial. We compared the performances of IPW-PP estimators with commonly used naive and baseline-adjusted per-protocol estimators, under different DGMs emulating pragmatic trials, comparing two sustained treatment strategies, possibly with a non-null effect. DGMs include (i) different roles of a baseline variable; whether future time-varying prognostic factors are impacted by past adherence; and whether the baseline variable is measured, (ii) whether adherence patterns observed in two arms are differential, and when we have access to measurements of adherence and confounders that are recorded infre- quently (sparsely). When baseline confounders are adjusted, we generally obtain unbiased estimates, but if some necessary variables are not measured, the IPW-PP estimator may still be preferable. High non-adherence patterns might negatively impact IPW-PP effect estimators, particularly when DGMs include confounding that may be influenced by previ- ous adherence history. We used the above estimators to analyze a case study from the Lipid Research Clinics Coronary Primary Prevention Trial data in the presence of non-adherence. \u0000Journal of Statistical Research 2022, Vol. 56, No. 2, pp.134-154 \u0000 \u0000 \u0000","PeriodicalId":90001,"journal":{"name":"Journal of statistical research","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44616622","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}

{"title":"Optimal allocation schemes in mixed ANCOVA models for longitudinal data","authors":"Xiaojian Xu, S. Sinha","doi":"10.3329/jsr.v56i2.67463","DOIUrl":"https://doi.org/10.3329/jsr.v56i2.67463","url":null,"abstract":"\u0000 \u0000 \u0000We discuss the construction of optimal allocation schemes for the linear mixed model with clustered outcomes or repeated measurements often encountered in longitudinal studies. We consider both treatment and covariate effects in the mixed model, where latent pro- cesses are used to describe random cluster or subject effects. A goal of optimal design schemes is to determine proportions of sample units allocated to each treatment for a given total sample size. We develop the optimal designs in a general setting using both D- and A- optimal design criteria. Specifically, we propose a two-stage design approach to deal with unknown parameters in the linear mixed model, where the variances of the random effects across the treatment groups are considered different. We study the empirical properties of the proposed designs using Monte Carlo simulations. An application is also provided using actual clinical data from a longitudinal study. \u0000Journal of Statistical Research, Vol 56, No 2, p101-114 \u0000 \u0000 \u0000","PeriodicalId":90001,"journal":{"name":"Journal of statistical research","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44865406","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}