Information and Divergence Measures.

The present Special Issue of Entropy, entitled Information and Divergence Measures, covers various aspects and applications in the general area of Information and Divergence Measures [...].

On stochastic dynamic modeling of incidence data.

In this paper, a Markov Regime Switching Model of Conditional Mean with covariates, is proposed and investigated for the analysis of incidence rate data. The components of the model are selected by both penalized likelihood techniques in conjunction with the Expectation Maximization algorithm, with the goal of achieving a high level of robustness regarding the modeling of dynamic behaviors of epidemiological data. In addition to statistical inference, Changepoint Detection Analysis is performed for the selection of the number of regimes, which reduces the complexity associated with Likelihood Ratio Tests. Within this framework, a three-phase procedure for modeling incidence data is proposed and tested via real and simulated data.

Contingency Table Analysis and Inference via Double Index Measures.

In this work, we focus on a general family of measures of divergence for estimation and testing with emphasis on conditional independence in cross tabulations. For this purpose, a restricted minimum divergence estimator is used for the estimation of parameters under constraints and a new double index (dual) divergence test statistic is introduced and thoroughly examined. The associated asymptotic theory is provided and the advantages and practical implications are explored via simulation studies.

Robust Model Selection Criteria Based on Pseudodistances.

In this paper, we introduce a new class of robust model selection criteria. These criteria are defined by estimators of the expected overall discrepancy using pseudodistances and the minimum pseudodistance principle. Theoretical properties of these criteria are proved, namely asymptotic unbiasedness, robustness, consistency, as well as the limit laws. The case of the linear regression models is studied and a specific pseudodistance based criterion is proposed. Monte Carlo simulations and applications for real data are presented in order to exemplify the performance of the new methodology. These examples show that the new selection criterion for regression models is a good competitor of some well known criteria and may have superior performance, especially in the case of small and contaminated samples.

Statistical inference for a general class of distributions with time-varying parameters.

In this article we are interested in a general class of distributions for independent not necessarily identically distributed random variables, closed under minima, that includes a number of discrete and continuous distributions like the Geometric, Exponential, Weibull or Pareto. The main parameter involved in this class of distributions is assumed to be time varying with several possible modeling options. This is of particular interest in reliability and survival analysis for describing the time to event or failure. The maximum likelihood estimation of the parameters is addressed and the asymptotic properties of the estimators are discussed. We provide real and simulated examples and we explore the accuracy of the estimating procedure as well as the performance of classical model selection criteria in choosing the correct model among a number of competing models for the time-varying parameters of interest.

Periodic-type auto-regressive moving average modeling with covariates for time-series incidence data via changepoint detection.

When it comes to incidence data, most of the work on this field focuses on the modeling of nonextreme periods. Several attempts have been made and a variety of techniques are available to achieve so. In this work, in order to model not only the nonextreme periods but also capture the behavior of the whole time-series, we make use of a dataset on influenza-like illness rate for Greece, for the period 2014-2016. The identification of extreme periods is made possible via changepoint detection analysis and model selection techniques are developed in order to identify the optimal periodic-type auto-regressive moving average model with covariates that best describes the pattern of the time-series. In addition, in the context of incidence data modeling, an advanced algorithm was developed in order to improve the accuracy of the selected model. The derived results are satisfactory since the changepoint method seems to identify correctly the extreme periods, and the selected model: (1) estimates accurately the influenza-like illness syndrome morbidity burden in the case of Greece, and (2) captures satisfactorily enough the behavior of the whole time-series.

MeDIP real-time qPCR of maternal peripheral blood reliably identifies trisomy 21.

OBJECTIVE: To reevaluate the efficiency of the 12 differentially methylated regions (DMRs) used in the methylated DNA immunoprecipitation (MeDIP) real-time quantitative polymerase chain reaction (real-time qPCR) based approach, develop an improved version of the diagnostic formula and perform a larger validation study. METHODS: Twelve selected DMRs were checked for copy number variants in the Database of Genomic Variants. The DMRs located within copy number variants were excluded from the analysis. One hundred and seventy-five maternal peripheral blood samples were used to reconstruct and evaluate the new diagnostic formula and for a larger-scale blinded validation study using MeDIP real-time qPCR. RESULTS: Seven DMRs entered the final model of the prediction equation and a larger blinded validation study demonstrated 100% sensitivity and 99.2% specificity. No significant evidence for association was observed between cell free fetal DNA concentration and D value. CONCLUSION: The MeDIP real-time qPCR method for noninvasive prenatal diagnosis of trisomy 21 was confirmed and revalidated in 175 samples with satisfactory results demonstrating that it is accurate and reproducible. We are currently working towards simplification of the method to make it more robust and therefore easily, accurately, and rapidly reproduced and adopted by other laboratories. Nevertheless, larger scale validation studies are necessary before the MeDIP real-time qPCR-based method could be applied in clinical practice.

A new non-invasive prenatal diagnosis of Down syndrome through epigenetic markers and real-time qPCR.

INTRODUCTION: Non-invasive prenatal diagnosis (NIPD) of Down syndrome is rapidly evolving. Currently, two applications for NIPD of Down syndrome have been developed with potential and have displayed positive results; the NIPD using next-generation sequencing technologies and the NIPD using the methylated DNA immunoprecipitation (MeDIP) real-time quantitative polymerase chain reaction (qPCR). AREAS COVERED: The MeDIP real-time qPCR approach is based on the identification of differentially methylated regions (DMRs) and their use for discriminating normal from Down syndrome cases. DMRs were identified using high-resolution oligo-arrays. A subgroup of DMRs was selected for further investigation. Through the design of a discriminant equation which combines the results obtained from different DMRs, normal and abnormal cases are correctly classified indicating 100% sensitivity and specificity. EXPERT OPINION: Previous studies have also identified DMRs between non-pregnant female blood and placental DNA. However, these methods have been associated with a number of limitations including the low sensitivity and/or specificity of the assays, the limited number of identified DMRs or methylation sensitive sites and SNPs located on DMRs. These limitations have been overawed by the development of the MeDIP real-time qPCR-based methodology.

Fetal-specific DNA methylation ratio permits noninvasive prenatal diagnosis of trisomy 21.

The trials performed worldwide toward noninvasive prenatal diagnosis (NIPD) of Down's syndrome (or trisomy 21) have shown the commercial and medical potential of NIPD compared to the currently used invasive prenatal diagnostic procedures. Extensive investigation of methylation differences between the mother and the fetus has led to the identification of differentially methylated regions (DMRs). In this study, we present a strategy using the methylated DNA immunoprecipitation (MeDiP) methodology in combination with real-time quantitative PCR (qPCR) to achieve fetal chromosome dosage assessment, which can be performed noninvasively through the analysis of fetal-specific DMRs. We achieved noninvasive prenatal detection of trisomy 21 by determining the methylation ratio of normal and trisomy 21 cases for each tested fetal-specific DMR present in maternal peripheral blood, followed by further statistical analysis. The application of this fetal-specific methylation ratio approach provided correct diagnosis of 14 trisomy 21 and 26 normal cases.

Variable selection strategies in survival models with multiple imputations.

In this paper, the variable selection strategies (criteria) are thoroughly discussed and their use in various survival models is investigated. The asymptotic efficiency property, in the sense of Shibata Ann Stat 8: 147-164, 1980, of a class of variable selection strategies which includes the AIC and all criteria equivalent to it, is established for a general class of survival models, such as parametric frailty or transformation models and accelerated failure time models, under minimum conditions. Furthermore, a multiple imputations method is proposed which is found to successfully handle censored observations and constitutes a competitor to existing methods in the literature. A number of real and simulated data are used for illustrative purposes.