Communication in Statistics- Theory and Methods

Janamiri, Fariba; Rasekh, Abdolrahman; Chaji, Alireza; Babadi, Babak

doi: 10.1080/03610926.2021.2003402pmid: N/A

Abstract In this paper, we concentrate on the generalized maximum entropy (GME) estimators. The aim is to improve the problem of multicollinearity in the linear mixed models (LMMs). Then the asymptotic properties of these estimators will be derived. Also, we obtain the Ridge-GME estimators, which combines ridge regression and GME, to enhance the problem of the traditional ridge regression and GME method in these models. Finally, a simulation study and a numerical example have been conducted to show the superiority of the Ridge-GME estimator over the ridge estimator (RE) and the maximum likelihood (ML) estimators.

Ghosh, Indranil

doi: 10.1080/03610926.2021.2004422pmid: N/A

Abstract The usefulness of a hidden truncated Pareto (type II) model along with its’ inference under both the classical and Bayesian paradigm have been discussed in the literature in great details. In the multivariate set-up, some discussions are made that are primarily based on constructing a multivariate hidden truncated Pareto (type II) models with — single variable truncation or more than one variable truncation. However, in all such previous discussions regarding bivariate hidden truncated Pareto models, in the classical estimation set-up, large bias and standard error values for the truncation parameter(s) as well as for the other parameters have been observed, and no discussion was made to address this issue. In this article, we try to address this issue of large bias values by considering constrained optimization via linear/non-linear transformation of the parameters following the strategy as proposed (the reference is given in Section 3), in efficiently implementing Newton-Raphson optimization algorithm in R. This plays a major motivation for the present paper. We also derive the observed Fisher Information Matrix. For illustrative purposes, we provide a simulation study to address this issue. A real-life data set is also re-analyzed to study the utility of such two-sided hidden truncation Pareto (type II) models.

Chu, Fang-I; Wang, Yuedong

doi: 10.1080/03610926.2021.2004423pmid: 37588769

Abstract Vascular access for hemodialysis is of paramount importance. Although studies have found that central venous catheter (CVC) is often associated with poor outcomes and switching to arteriovenous fistula (AVF) and arteriovenous grafts (AVG) is beneficial, it has not been fully elucidated how the effect of switching of access on outcomes changes over time and whether the effect depends on switching time. In this article, we propose to relate the observed survival time for patients without access change and the counterfactual time for patients with access change using an AFT model with time-varying effects. The flexibility of AFT model allows us to account for baseline effect and the prognostic effect from covariates at access change while estimating the effect of access change. The effect of access change over time is modeled nonparametrically using a cubic spline function. Simulation studies show excellent performance. Our methods are applied to investigate the effect of vascular access change over time in dialysis patients. It is concluded that the benefit of switching from CVC to AVG depends on the time of switching, the sooner the better.

Xiong, Juan; He, Wenqing

doi: 10.1080/03610926.2021.2004424pmid: N/A

Abstract Modern gene expression technologies, such as microarray and the next generation RNA sequencing, enable simultaneous measurement of expressions of a large number of genes, and therefore represent important tools in the personalized medicine research for improving the patient survival prediction accuracy. However, survival analysis with gene expression data can be challenging due to the high dimensionality. Proper identification of survival relevant genes is thus imperative for building suitable prediction models. In spite of the fact that gene expressions are typically subject to measurement errors introduced from the complex experimental procedure, the issue of measurement error is often ignored in survival gene identifications. In this article, the effect of measurement error on the identification of survival relevant genes is explored under the accelerated failure time model setting. Survival relevant genes are identified by regularizing the weighted least square estimator with the adaptive LASSO penalty. The simulation-extrapolation method is incorporated to adjust for the impact of measurement error on gene identification. The performance of the proposed method is assessed by simulation studies and the utility of the proposed method is illustrated by a real data set collected from the diffuse large-B-cell lymphoma study. The results show that the proposed method yields better prediction models than traditional methods which ignore measurement error in gene expressions.

Volf, Petr; Kouřim, Tomáš

doi: 10.1080/03610926.2021.2004425pmid: N/A

Abstract This contribution deals with a model of one-dimensional Bernoulli-like random walk with the position of the walker controlled by varying transition probabilities. These probabilities depend explicitly on the previous move of the walker and, therefore, implicitly on the entire walk history. Hence, the walk is not Markov. The article follows on the recent work of the authors, the models presented here describe how the logits of transition probabilities are changing in dependence on the last walk step. In the basic model this development is controlled by parameters. In the more general setting these parameters are allowed to be time-dependent. The contribution focuses mainly on reliable estimation of model components via the MLE procedures in the framework of the generalized linear models.

Yan, Hui; Xu, Lifeng; Zhang, Shaoyi

doi: 10.1080/03610926.2021.2004426pmid: N/A

Abstract In this article, an almost sure central limit theorem for non-stationary Markov chains on a general state space is established by using the techniques of shift operator and harmonic function.

Vijaya Laxmi, P.; Girija Bhavani, E.; George, Andwilile Abrahamu

doi: 10.1080/03610926.2021.2004427pmid: N/A

Abstract This paper investigates a multi-server Markovian queueing system with an optional service, working vacations of servers and retention of reneged customers. When all the servers are busy, customers may renege from the system with probability r 1 or may remain in the queue with probability After working vacation completion, the c servers return back to regular busy period if there is at least one customer in the system; otherwise, they continue the vacation. The steady-state probabilities of the model are obtained using the matrix geometric method. Numerical investigations with cost model and performance measures are illustrated with tables and graphs.

Slaoui, Yousri

doi: 10.1080/03610926.2021.2004428pmid: N/A

Abstract In this paper, we propose and investigate a new kernel regression estimators based on the two-time-scale stochastic approximation algorithm in the case of independent functional data. We study the properties of the proposed recursive estimators and compare them with the recursive estimators based on single-time-scale stochastic algorithm proposed by Slaoui and to the non-recursive estimator proposed by Slaoui. It turns out that, with an adequate choice of the parameters, the proposed two-time-scale estimators perform better than the recursive estimators constructed using single-time-scale stochastic algorithm. We corroborate these theoretical results through some simulations and two real datasets.

Mao, Guangcai; Zhang, Jing

doi: 10.1080/03610926.2021.2005098pmid: N/A

Abstract The hazard rate function plays an important role in survival analysis. Extensive work has been carried out for building the point estimation of hazard rate function but limited work has been done for constructing its confidence bands, which is a fundamental issue in statistical inference. To fill in this gap, we focus on investigating this problem for right censored survival data in this paper. We first propose a debiased estimation for hazard rate function, based on which, we further construct the pointwise and simultaneous confidence bands for hazard rate function. We establish some theoretical properties of the proposed method, including the convergence rate and asymptotic distributions. The finite-sample performance of the proposed procedure is evaluated via both simulation studies and an application to a real data from the breast cancer study.

Kharazmi, Omid; Balakrishnan, Narayanaswamy

doi: 10.1080/03610926.2021.2005100pmid: N/A

Abstract In this work, we propose cumulative residual information generating (CRIG) and relative cumulative residual information generating (RCRIG) measures and then establish some of their properties. A new divergence measure based on the CRIG function is proposed to measure the closeness between two survival functions as well as a cumulative residual Kullback-Leibler divergence. We also present Jensen-cumulative residual information generating function, whose derivatives generate some new cumulative information measures such as Jensen-cumulative residual Taneja entropy, Jensen-fractional cumulative residual entropy and Jensen-Gini mean difference measure. We further show that the Jensen-cumulative residual information generating function can be expressed as a mixture of two versions of the proposed new divergence measure.