Inequalities for sums of squares of reranked differences involving ties and missing data

An inequality for the sum of squares of rank differences associated with Spearman’s rank correlation coefficient, when ties and missing data are present in both rankings, was established numerically in Loukas and Papaioannou (1991 Loukas, S., Papaioannou, T. (1991). Rank correlation inequalities with ties and missing data. Stat. Probab. Lett. 11:53–56.[Crossref], [Web of Science ®] , [Google Scholar]). That inequality is improved and generalized.     

Empirical Likelihood Inference for Longitudinal Data with Missing Response Variables and Error-Prone Covariates

We consider statistical inference for longitudinal partially linear models when the response variable is sometimes missing with missingness probability depending on the covariate that is measured with error. The block empirical likelihood procedure is used to estimate the regression coefficients and residual adjusted block empirical likelihood is employed for the baseline function. This leads us to prove a nonparametric version of Wilk's theorem. Compared with methods based on normal approximations, our proposed method does not require a consistent estimators for the asymptotic variance and bias. An application to a longitudinal study is used to illustrate the procedure developed here. A simulation study is also reported.     

Inferring gene regulatory networks by an order independent algorithm using incomplete data sets

Analyzing incomplete data for inferring the structure of gene regulatory networks (GRNs) is a challenging task in bioinformatic. Bayesian network can be successfully used in this field. k-nearest neighbor, singular value decomposition (SVD)-based and multiple imputation by chained equations are three fundamental imputation methods to deal with missing values. Path consistency (PC) algorithm based on conditional mutual information (PCA–CMI) is a famous algorithm for inferring GRNs. This algorithm needs the data set to be complete. However, the problem is that PCA–CMI is not a stable algorithm and when applied on permuted gene orders, different networks are obtained. We propose an order independent algorithm, PCA–CMI–OI, for inferring GRNs. After imputation of missing data, the performances of PCA–CMI and PCA–CMI–OI are compared. Results show that networks constructed from data imputed by the SVD-based method and PCA–CMI–OI algorithm outperform other imputation methods and PCA–CMI. An undirected or partially directed network is resulted by PC-based algorithms. Mutual information test (MIT) score, which can deal with discrete data, is one of the famous methods for directing the edges of resulted networks. We also propose a new score, ConMIT, which is appropriate for analyzing continuous data. Results shows that the precision of directing the edges of skeleton is improved by applying the ConMIT score.     

Estimation in Regressive Logistic Regression Analyses of Familial Data with Missing Outcomes

This paper examines a number of methods of handling missing outcomes in regressive logistic regression modelling of familial binary data, and compares them with an EM algorithm approach via a simulation study. The results indicate that a strategy based on imputation of missing values leads to biased estimates, and that a strategy of excluding incomplete families has a substantial effect on the variability of the parameter estimates. Recommendations are made which depend, amongst other factors, on the amount of missing data and on the availability of software.     

Effect of missing plots in some response surface designs

The effect of one or more missing observations for response surface designs arranged in blocks are examined in this paper. The resu lts as applied to a central composite design with orthogonal blocking, and an equirdial design with orthogonal blocking, are reported.     

Robust estimation of dropout models using hierarchical likelihood

It is very well known that analyses for missing data depend on untestable assumptions. As a consequence, in such settings, sensitivity analyses are often sensible. One such class of analyses assesses the dependence of conclusions on an explicit missing value mechanism. Inevitably, there is an association between such dependence and the actual (but unknown) distribution of the missing data. In a particular parametric framework for dropout in this paper, an approach is presented that reduces (but never removes) the impact of incorrect assumptions on the form of the association. It is shown how these models can be formulated and fitted relatively simply using hierarchical likelihood. These are applied directly to an example involving mastitis in dairy cattle, and an extensive simulation study is described to show the properties of the methods.     

Exact maximum likelihood for incomplete data from a correlated gaussian process

In many situations in which a variable is measured at locations in time or space the observed data can be regarded as incomplete, the missing data sites perhaps completing a regular pattern such as a rectangular grid. In this paper general methods not dependent on the sequential nature of time are considered for estimating the parameters of Gaussian processes. An example is given.     

Multivariate Linear Regression Model with Elliptically Contoured Distributed Errors and Monotone Missing Dependent Variables

In this article, the multivariate linear regression model is studied under the assumptions that the error term of this model is described by the elliptically contoured distribution and the observations on the response variables are of a monotone missing pattern. It is primarily concerned with estimation of the model parameters, as well as with the development of the likelihood ratio test in order to examine the existence of linear constraints on the regression coefficients. An illustrative example is presented for the explanation of the results.     

The impact of missing data on the results of a schizophrenia study

Missing data pose a serious challenge to the integrity of randomized clinical trials, especially of treatments for prolonged illnesses such as schizophrenia, in which long‐term impact assessment is of great importance, but the follow‐up rates are often no more than 50%. Sensitivity analysis using Bayesian modeling for missing data offers a systematic approach to assessing the sensitivity of the inferences made on the basis of observed data. This paper uses data from an 18‐month study of veterans with schizophrenia to demonstrate this approach. Data were obtained from a randomized clinical trial involving 369 patients diagnosed with schizophrenia that compared long‐acting injectable risperidone with a psychiatrist's choice of oral treatment. Bayesian analysis utilizing a pattern‐mixture modeling approach was used to validate the reported results by detecting bias due to non‐random patterns of missing data. The analysis was applied to several outcomes including standard measures of schizophrenia symptoms, quality of life, alcohol use, and global mental status. The original study results for several measures were confirmed against a wide range of patterns of non‐random missingness. Robustness of the conclusions was assessed using sensitivity parameters. The missing data in the trial did not likely threaten the validity of previously reported results. Copyright © 2014 John Wiley & Sons, Ltd.     

Regression Analysis of Length‐biased and Right‐censored Failure Time Data with Missing Covariates

Length‐biased and right‐censored failure time data arise from many fields, and their analysis has recently attracted a great deal of attention. Two examples of the areas that often produce such data are epidemiological studies and cancer screening trials. In this paper, we discuss regression analysis of such data in the presence of missing covariates, for which no established inference procedure seems to exist. For the problem, we consider the data arising from the proportional hazards model and propose two inverse probability weighted estimation procedures. The asymptotic properties of the resulting estimators are established, and the extensive simulation study conducted for the evaluation of the proposed methods suggests that they work well for practical situations.