Estimation of the parameters of the Gompertz distribution under the first failure-censored sampling plan

This article presents the goodness-of-fit tests for the Laplace distribution based on its maximum entropy characterization result. The critical values of the test statistics estimated by Monte Carlo simulations are tabulated for various window and sample sizes. The test statistics use an entropy estimator depending on the window size; so, the choice of the optimal window size is an important problem. The window sizes for yielding the maximum power of the tests are given for selected sample sizes. Power studies are performed to compare the proposed tests with goodness-of-fit tests based on the empirical distribution function. Simulation results report that entropy-based tests have consistently higher power than EDF tests against almost all alternatives considered.     

Influence Analysis in Principal Component Analysis Through Power-Series Expansions

ABSTRACT

In influence analysis several problems arise in the field of Principal Components when applying different sample versions. Among these are the difficulty of determining a certain correspondence between the eigenvalues before and after the deletion of observations, the choice of the sign of the eigenvectors and the computational problem derived from the resolution of a great number of eigenproblems. In this article, such problems are discussed from the joint influence point of view and a solution is proposed by using approximations. Furthermore, the influence on a new parameter of interest is introduced: the proportion of variance explained by a set of principal components.     

Joint Mean-Covariance Models with Applications to Longitudinal Data in Partially Linear Model

Semiparametric regression models and estimating covariance functions are very useful in longitudinal study. Unfortunately, challenges arise in estimating the covariance function of longitudinal data collected at irregular time points. In this article, for mean term, a partially linear model is introduced and for covariance structure, a modified Cholesky decomposition approach is proposed to heed the positive-definiteness constraint. We estimate the regression function by using the local linear technique and propose quasi-likelihood estimating equations for both the mean and covariance structures. Moreover, asymptotic normality of the resulting estimators is established. Finally, simulation study and real data analysis are used to illustrate the proposed approach.     

The Search for Coordination: The Case of the Central Policy Review Staff and Social Policy Planning, 1971–77

The Central Policy Review Staff (CPRS) attempted to create an ambitious strategy for the horizontal coordination of social policy in the UK during the early 1970s. The attempt – inspired largely by planning, programming and budgeting systems – was a failure, and gave way to a much modified ‘joint framework for social policies’ in 1975. Recent research has compared the CPRS's joint framework approach to the Labour government's promotion of ‘joined‐up government’ (JUG) in the 2000s. This article provides a case study of the CPRS's work on social policy planning, using archival sources. The case study addresses themes that remain significant, particularly approaches to and the politics of horizontal coordination. Finally, the article makes a modest attempt to signal the differences between the 1970s' approach and ‘JUG’.     

Joint models for multiple longitudinal processes and time-to-event outcome

ABSTRACT

Joint models are statistical tools for estimating the association between time-to-event and longitudinal outcomes. One challenge to the application of joint models is its computational complexity. Common estimation methods for joint models include a two-stage method, Bayesian and maximum-likelihood methods. In this work, we consider joint models of a time-to-event outcome and multiple longitudinal processes and develop a maximum-likelihood estimation method using the expectation–maximization algorithm. We assess the performance of the proposed method via simulations and apply the methodology to a data set to determine the association between longitudinal systolic and diastolic blood pressure measures and time to coronary artery disease.     

基于贝叶斯推断的HIV非线性混合效应联合模型研究

 在纵向数据研究中,混合效应模型的随机误差通常采用正态性假设。而诸如病毒载量和CD4细胞数目等病毒性数据通常呈现偏斜性,因此正态性假设可能影响模型结果甚至导致错误的结论。在HIV动力学研究中,病毒响应值往往与协变量相关,且协变量的测量值通常存在误差,为此论文中联立协变量过程建立具有偏正态分布的非线性混合效应联合模型,并用贝叶斯推断方法估计模型的参数。由于协变量能够解释个体内的部分变化,因此协变量过程的模型选择对病毒载量的拟合效果有重要的影响。该文提出了一次移动平均模型作为协变量过程的改进模型,比较后发现当协变量采用移动平均模型时,病毒载量模型的拟合效果更好。该结果对协变量模型的研究具有重要的指导意义。     

合力构建纵横结合的大学生思想政治教育新平台

当前，随着国内外政治、经济环境的复杂多变和我国高等教育事业的迅速发展，大学生思想政治教育的对象、内容和任务都发生了深刻变化。这就要求高校教育工作者必须了解大学生的发展需求，充分发挥社会、学校、家庭和党团学组织的合力作用，始终保持课内外培养目标的一致性。同时，依托良好校园文化的建设，积极推进现代化工具介入，形成纵向到底、横向到边三位一体、有机结合的思想政治教育网络覆盖体系，实现多渠道渗透的教育合力目标，从而促进大学生思想政治教育的创新发展。     

Multi-output profit efficiency and directional distance functions

We extend a recently developed DEA methodology for cost efficiency analysis towards profit efficiency settings. This establishes a novel DEA toolkit for profit efficiency assessments in situations with multiple inputs and multiple outputs. A distinguishing feature of our methodology is that it assumes output-specific production technologies. In addition, the methodology accounts for the use of joint inputs, and explicitly includes information on the allocation of inputs to individual outputs. We also establish a dual relationship between our multi-output profit inefficiency measure and a technical inefficiency measure that takes the form of a multi-output directional distance function. Finally, we demonstrate the empirical usefulness of our methodology by an empirical application to a large service company.     

Joint estimation for the parameters of the extreme value distributions

The purpose of this paper is to provide a method for constructing exact joint confidence regions for the parameters of type I (maximum) and type I (minimum) extreme value distributions. Joint confidence regions for the parameters of Weibull distributions are also discussed. The calculation for these joint confidence regions requires a small computer program.