An evaluation of ridge estimator in linear mixed models: an example from kidney failure data

This paper is concerned with the ridge estimation of fixed and random effects in the context of Henderson's mixed model equations in the linear mixed model. For this purpose, a penalized likelihood method is proposed. A linear combination of ridge estimator for fixed and random effects is compared to a linear combination of best linear unbiased estimator for fixed and random effects under the mean-square error (MSE) matrix criterion. Additionally, for choosing the biasing parameter, a method of MSE under the ridge estimator is given. A real data analysis is provided to illustrate the theoretical results and a simulation study is conducted to characterize the performance of ridge and best linear unbiased estimators approach in the linear mixed model.     

Computationally feasible estimation of the covariance structure in generalized linear mixed models

In this paper, we discuss how a regression model, with a non-continuous response variable, which allows for dependency between observations, should be estimated when observations are clustered and measurements on the subjects are repeated. The cluster sizes are assumed to be large. We find that the conventional estimation technique suggested by the literature on generalized linear mixed models (GLMM) is slow and sometimes fails due to non-convergence and lack of memory on standard PCs. We suggest to estimate the random effects as fixed effects by generalized linear model and to derive the covariance matrix from these estimates. A simulation study shows that our proposal is feasible in terms of mean-square error and computation time. We recommend that our proposal be implemented in the software of GLMM techniques so that the estimation procedure can switch between the conventional technique and our proposal, depending on the size of the clusters.     

New modified moment estimators for the two-parameter weighted Lindley distribution

We propose a modification of the moment estimators for the two-parameter weighted Lindley distribution. The modification replaces the second sample moment (or equivalently the sample variance) by a certain sample average which is bounded on the unit interval for all values in the sample space. In this method, the estimates always exist uniquely over the entire parameter space and have consistency and asymptotic normality over the entire parameter space. The bias and mean squared error of the estimators are also examined by means of a Monte Carlo simulation study, and the empirical results show the small-sample superiority in addition to the desirable large sample properties. Monte Carlo simulation study showed that the proposed modified moment estimators have smaller biases and smaller mean-square errors than the existing moment estimators and are compared favourably with the maximum likelihood estimators in terms of bias and mean-square error. Three illustrative examples are finally presented.     

The feasible generalized restricted ridge regression estimator

The presence of autocorrelation in errors and multicollinearity among the regressors have undesirable effects on the least-squares regression. There are a wide range of methods which are proposed to overcome the usefulness of the ordinary least-squares estimator or the generalized least-squares estimator, such as the Stein-rule, restricted least-squares or ridge estimator. Therefore, we introduce a new feasible generalized restricted ridge regression (FGRR) estimator to examine multicollinearity and autocorrelation problems simultaneously for the general linear regression model. We also derive some statistical properties of the FGRR estimator and comparisons have been conducted using matrix mean-square error. Moreover, a Monte Carlo simulation experiment is performed to investigate the performance of the proposed estimator over the others.     

A recursive approach for estimating missing observations in an univariate time series

A linear recursive technique that does not use the Kalman filter approach is proposed to estimate missing observations in an univariate time series. It is assumed that the series follows an invertible ARIMA model. The procedure is based on the restricted forecasting approach, and the recursive linear estimators are optimal in terms of minimum mean-square error.     

The Scale Invariant Wigner Spectrum Estimation of Gaussian Locally Self-Similar Processes

We study locally self-similar processes (LSSPs) in Silverman’s sense. By deriving the minimum mean-square optimal kernel within Cohen’s class counterpart of time–frequency representations, we obtain an optimal estimation for the scale invariant Wigner spectrum (SIWS) of Gaussian LSSPs. The class of estimators is completely characterized in terms of kernels, so the optimal kernel minimizes the mean-square error of the estimation. We obtain the SIWS estimation for two cases: global and local, where in the local case, the kernel is allowed to vary with time and frequency. We also introduce two generalizations of LSSPs: the locally self-similar chirp process and the multicomponent LSSP, and obtain their optimal kernels. Finally, the performance and accuracy of the estimation is studied via simulation.     

Multivariate-multiple circular regression

We introduce a fully model-based approach of studying functional relationships between a multivariate circular-dependent variable and several circular covariates, enabling inference regarding all model parameters and related prediction. Two multiple circular regression models are presented for this approach. First, for an univariate circular-dependent variable, we propose the least circular mean-square error (LCMSE) estimation method, and asymptotic properties of the LCMSE estimators and inferential methods are developed and illustrated. Second, using a simulation study, we provide some practical suggestions for model selection between the two models. An illustrative example is given using a real data set from protein structure prediction problem. Finally, a straightforward extension to the case with a multivariate-dependent circular variable is provided.     

Variable Selection in Linear Mixed Models Using an Extended Class of Penalties

There is an emerging need to advance linear mixed model technology to include variable selection methods that can simultaneously choose and estimate important effects from a potentially large number of covariates. However, the complex nature of variable selection has made it difficult for it to be incorporated into mixed models. In this paper we extend the well known class of penalties and show that they can be integrated succinctly into a linear mixed model setting. Under mild conditions, the estimator obtained from this mixed model penalised likelihood is shown to be consistent and asymptotically normally distributed. A simulation study reveals that the extended family of penalties achieves varying degrees of estimator shrinkage depending on the value of one of its parameters. The simulation study also shows there is a link between the number of false positives detected and the number of true coefficients when using the same penalty. This new mixed model variable selection (MMVS) technology was applied to a complex wheat quality data set to determine significant quantitative trait loci (QTL).     

On comutativity of design and model expectations in randomized response surveys

Interchangability of the order of design expectation ( E_p ) and model expectation ( E_R ) under randomized response ( RR ) surveys for finding expectation and mean-square error of a linear estimator e found to be permissible for a non-informative sampling design. The non-comutative property established by Chaudhuri & Adhikary ( 1990 ) under probability proportional to size with replacement ( PPSWR ) sampling is disproved.     

Mean likelihood estimators

The use of Mathematica in deriving mean likelihood estimators is discussed. Comparisons are made between the mean likelihood estimator, the maximum likelihood estimator, and the Bayes estimator based on a Jeffrey's noninformative prior. These estimators are compared using the mean-square error criterion and Pitman measure of closeness. In some cases it is possible, using Mathematica, to derive exact results for these criteria. Using Mathematica, simulation comparisons among the criteria can be made for any model for which we can readily obtain estimators.In the binomial and exponential distribution cases, these criteria are evaluated exactly. In the first-order moving-average model, analytical comparisons are possible only for n = 2. In general, we find that for the binomial distribution and the first-order moving-average time series model the mean likelihood estimator outperforms the maximum likelihood estimator and the Bayes estimator with a Jeffrey's noninformative prior. Mathematica was used for symbolic and numeric computations as well as for the graphical display of results. A Mathematica notebook which provides the Mathematica code used in this article is available: http://www.stats.uwo.ca/mcleod/epubs/mele. Our article concludes with our opinions and criticisms of the relative merits of some of the popular computing environments for statistics researchers.     

A Joint Modeling Approach for Longitudinal Outcomes and Non-ignorable Dropout under Population Heterogeneity in Mental Health Studies

The paper proposes a joint mixture model to model non-ignorable drop-out in longitudinal cohort studies of mental health outcomes. The model combines a (non)-linear growth curve model for the time-dependent outcomes and a discrete-time survival model for the drop-out with random effects shared by the two sub-models. The mixture part of the model takes into account population heterogeneity by accounting for latent subgroups of the shared effects that may lead to different patterns for the growth and the drop-out tendency. A simulation study shows that the joint mixture model provides greater precision in estimating the average slope and covariance matrix of random effects. We illustrate its benefits with data from a longitudinal cohort study that characterizes depression symptoms over time yet is hindered by non-trivial participant drop-out.KEYWORDS: Latent growth curve, MNAR drop-out, survival analysis, finite mixture model, mental health     

Semiparametric Efficient Estimation for a Class of Generalized Proportional Odds Cure Models

We present a mixture cure model with the survival time of the "uncured" group coming from a class of linear transformation models, which is an extension of the proportional odds model. This class of model, first proposed by Dabrowska and Doksum (1988), which we term "generalized proportional odds model," is well suited for the mixture cure model setting due to a clear separation between long-term and short-term effects. A standard expectation-maximization algorithm can be employed to locate the nonparametric maximum likelihood estimators, which are shown to be consistent and semiparametric efficient. However, there are difficulties in the M-step due to the nonparametric component. We overcome these difficulties by proposing two different algorithms. The first is to employ an majorize-minimize (MM) algorithm in the M-step instead of the usual Newton-Raphson method, and the other is based on an alternative form to express the model as a proportional hazards frailty model. The two new algorithms are compared in a simulation study with an existing estimating equation approach by Lu and Ying (2004). The MM algorithm provides both computational stability and efficiency. A case study of leukemia data is conducted to illustrate the proposed procedures.     

国民收入流量矩阵的编制与预测方法研究

本文首先在国家统计局公布的资金流量（实物交易）表的基础上编制了1992-2008年的“部门×交易(S-by-T)”和“部门×部门(S-by-S)”国民收入流量矩阵,设计了一个国民收入流量组合预测模型（The combination forecasting model for national income flow. NCFM）,表述了编表的内容、方法、步骤,配合该模型又设计了国民收入动态均衡模型（The dynamic equilibrium model for national income flow. NDEM）和单方程模型对主要控制总量进行预测,采用状态空间模型对所预测的总量进行了部门和交易分解,用DRAS法编制了2009和2010年的“S-by -T”国民收入流量矩阵的延长表,用“部门收入转移法（SITM）”编制了各年度的“S-by-S”表。最后用Theil’s U、SWAD和STPE三种方法分别对数据质量进行了检验。     

Birth forecasting based on birth order probabilities,with application to U.S. data

"A model for birth forecasting based on prediction of the so-called 'birth order probabilities' is constructed. The relation between this model and recent models of fertility prediction is derived. Birth forecasts with approximate probability limits for the U.S. for the period 1983-1997 are generated. The performance of the proposed model in predicting future fertility is tested by fitting time series models to part of the available series (1917-1982) and ultimately generating birth forecasts for the remainder of the period, then comparing these forecasts with the actual data." The accuracy of the fertility forecasts made are compared with those made by other methods.     

Penalized contrast estimation in functional linear models with circular data

Our aim is to estimate the unknown slope function in the functional linear model when the response Y is real and the random function X is a second-order stationary and periodic process. We obtain our estimator by minimizing a standard (and very simple) mean-square contrast on linear finite dimensional spaces spanned by trigonometric bases. Our approach provides a penalization procedure which allows to automatically select the adequate dimension, in a non-asymptotic point of view. In fact, we can show that our penalized estimator reaches the optimal (minimax) rate of convergence in the sense of the prediction error. We complete the theoretical results by a simulation study and a real example that illustrates how the procedure works in practice.     

Multiresponse surface models with random block effects

A multiresponse experiment is one in which a number of responses are measured for each setting of a group of input (control) variables. Quite often, the experimental units are subdivided into groups — or blocks — in order to control an extraneous source of variation. This necessitates adding block effects to the hypothesized multiresponse surface model, which typically contains fixed polynomial effects. Khuri and Valeroso (1998, J. Statist. Plann. Inference, Vol. 73, pp. 7–20) discussed the analysis of such a model when the block effects are considered fixed. There are many situations, however, where these effects should more appropriately be treated as random. In the present article, we address the analysis of a multiresponse model in the latter situations. In particular, we discuss the estimation of the model's polynomial effects in two cases:

• 1.The block effects are additive in the model.
• 2.The blocks have interactive effects with the polynomial portion of the model.

Multiresponse optimization in the presence of blocks will also be discussed. A numerical example is presented for illustrative purposes.     

A generalization of the Grizzle model to the estimation of treatment effects in crossover trials with non-compliance

Compliance with one specified dosing strategy of assigned treatments is a common problem in randomized drug clinical trials. Recently, there has been much interest in methods used for analysing treatment effects in randomized clinical trials that are subject to non-compliance. In this paper, we estimate and compare treatment effects based on the Grizzle model (GM) (ignorable non-compliance) as the custom model and the generalized Grizzle model (GGM) (non-ignorable non-compliance) as the new model. A real data set based on the treatment of knee osteoarthritis is used to compare these models. The results based on the likelihood ratio statistics and simulation study show the advantage of the proposed model (GGM) over the custom model (GGM).     

中国西部地区“四化”协调发展及其影响因素分析

基于中国西部地区省际面板数据,通过建立CCR-HR模型测算"四化"效率值及协调度,测算结果表明:西部地区"四化"协调发展水平整体不高,"四化"协调发展程度随着时间的发展越来越好,地域之间存在较大的差异;农业现代化发展水平的滞后是导致"四化"协调发展失衡的重要原因,以农业现代化为切入点,建立面板Ologit模型对"四化"协调发展的影响因素进行分析,分析结果显示:农业固定资产投资和农业劳动力对"四化"效率的提高有正向推动作用,农业机械化、有效灌溉面积、化肥施用量以及农业劳动力的增加可以提高"四化"协调度,农业劳动力不仅影响"四化"效率而且影响"四化"协调度。     

“丝绸之路经济带”西北五省区交通基础设施对经济增长的空间溢出效应

基于多种经济增长理论,选取影响"丝绸之路经济带"西北五省区多变量省际面板数据,构建交通基础设施对经济增长的空间溢出效应模型,分析结果为:2000—2014年"丝绸之路经济带"交通基础设施促进了经济增长;不考虑空间溢出效应的测算结果放大了交通基础设施的贡献率;外地交通基础设施对本地经济增长存在正的空间溢出效应,2010年以来此效应不断增强;劳动力、城市化水平成为推动经济增长的重要因素。     

Matched case–control data analyses with missing covariates

We consider methods for analysing matched case–control data when some covariates ( W ) are completely observed but other covariates ( X ) are missing for some subjects. In matched case–control studies, the complete-record analysis discards completely observed subjects if none of their matching cases or controls are completely observed. We investigate an imputation estimate obtained by solving a joint estimating equation for log-odds ratios of disease and parameters in an imputation model. Imputation estimates for coefficients of W are shown to have smaller bias and mean-square error than do estimates from the complete-record analysis.