A Multistage Procedure for Analyzing Unreplicated Factorial Experiments

A multistage procedure, which is based on the likelihood principle, is proposed to identify active effects in unreplicated factorial designs and their fractions. The proposed procedure controls the experimental error rate (EER) at any prespecified level in industrial and biomedical experiments. Extensive comparison with Lenth 's (1989) test is discussed.     

A Variance Components Model for Analyzing Repeated Measures Designs with Non-Stationary Error Structure

When data are collected in the form of multiple measurements on several subjects, they are often analyzed as repeated measures data with some stationary error structure assumed for the errors. For data with non-stationary error structure, the multivariate model is often used. The multivariate model imposes restrictions that are often not met in practice by data of such type. At the same time, they ignore valuable information in the data that are related to time dependencies and time relations. In this paper, we propose a model that is a reparametrization of the multivariate model and is suitable to analyze general repeated measures designs with non-stationary error structure. The model is shown to be a variance components model whose components are estimated using the method of maximum likelihood. Several other properties of the model are derived and discussed including tests of significance. Finally, an example on neurological data is included to demonstrate its application in biological sciences.     

Association of TNF-α polymorphisms (−857, −863 and −1031), TNF-α serum level and lipid profile with acne vulgaris

BackgroundAcne is an inflammatory condition principally affected by genetic and dietary factors. Investigation into functional polymorphisms of TNF-α gene and their association with acne vulgaris will be helpful in exploring genetic influence on skin immune mediated inflammatory events. In the present study, we analyzed association of TNF-α gene polymorphisms, its expression levels and lipid profiles in a large cohort of acne patients and controls.MethodsWe used PCR-RFLP to study association of TNF-α polymorphisms at −857C/T, −863C/A and −1031 T/C sites with acne vulgaris. Lipid profiles were measured using enzymatic end-point method. The serum levels of TNF-α and apolipoprotein a were measured using ELISA. NIH, LDlink was used to investigate patterns of linkage disequilibrium across south Asian reference genome (Punjabi from Lahore Pakistan).ResultsWe found that TNF-α −863 polymorphism is strongly associated with acne in overall population as well as in gender and severity based groups of acne patients. Polymorphisms at −863 and −1031 position were in linkage disequilibrium. Importantly, TNF-α serum level was significantly increased in acne patients with severe disease symptoms. Furthermore, levels of total cholesterol (TC) and triglycerides (TG) were significantly increased, whereas high density lipoprotein cholesterol (HDL-C) level was significantly decreased in acne patients. The levels of apolipoprotein a varied widely in studied populations and no significant difference was found in the analyzed groups.ConclusionIn conclusion, we found that TNF-α expression increases in acne patients affected by TNF-α polymorphisms, and that the lipid profile is specifically disrupted in acne patients.     

Finite Sample Properties of the Maximum Likelihood Estimator and of Likelihood Ratio Tests in Hidden Markov Models

Hidden Markov models were successfully applied in various fields of time series analysis, especially for analyzing ion channel recordings. The maximum likelihood estimator (MLE) has recently been proven to be asymptotically normally distributed. Here, we investigate finite sample properties of the MLE and of different types of likelihood ratio tests (LRTs) by means of simulation studies. The MLE is shown to reach the asymptotic behavior within sample sizes that are common for various applications. Thus, reliable estimates and confidence intervals can be obtained. We give an approximative scaling function for the estimation error for finite samples, and investigate the power of different LRTs suitable for applications to ion channels, including tests for superimposed hidden Markov processes. Our results are applied to physiological sodium channel data.     

Quasi Likelihood/Moment Method for Generalized and Restricted Generalized Poisson Regression Models and Its Application

This paper reviews the generalized Poisson regression model, the restricted generalized Poisson regression model and the mixed Poisson regression (negative binomial regression and Poisson inverse Gaussian regression) models which can be used for regression analysis of counts. The aim of this study is to demonstrate the quasi likelihood/moment method, which is used for estimation of the parameters of mixed Poisson regression models, also applicable to obtain the estimates of the parameters of the generalized Poisson regression and the restricted generalized Poisson regression models. Besides, at the end of this study an application related to this method for zoological data is given.     

The Generalised Estimating Equations: An Annotated Bibliography

The Generalised Estimating Equations (GEE) proposed by Liang and Zeger (1986) and Zeger and Liang (1986) have found considerable attention in the last ten years and several extensions have been proposed. In this annotated bibliography we describe the development of the GEE and its extensions during the last decade. Additionally, we discuss advantages and disadvantages of the different parametrisations that have been proposed in the literature. Furthermore, we review regression diagnostic techniques and approaches for dealing with missing data. We give an insight to the different fields of application in biometry. We also describe the software available for the GEE.     

SpeciesRax: A Tool for Maximum Likelihood Species Tree Inference from Gene Family Trees under Duplication,Transfer, and Loss

Species tree inference from gene family trees is becoming increasingly popular because it can account for discordance between the species tree and the corresponding gene family trees. In particular, methods that can account for multiple-copy gene families exhibit potential to leverage paralogy as informative signal. At present, there does not exist any widely adopted inference method for this purpose. Here, we present SpeciesRax, the first maximum likelihood method that can infer a rooted species tree from a set of gene family trees and can account for gene duplication, loss, and transfer events. By explicitly modeling events by which gene trees can depart from the species tree, SpeciesRax leverages the phylogenetic rooting signal in gene trees. SpeciesRax infers species tree branch lengths in units of expected substitutions per site and branch support values via paralogy-aware quartets extracted from the gene family trees. Using both empirical and simulated data sets we show that SpeciesRax is at least as accurate as the best competing methods while being one order of magnitude faster on large data sets at the same time. We used SpeciesRax to infer a biologically plausible rooted phylogeny of the vertebrates comprising 188 species from 31,612 gene families in 1 h using 40 cores. SpeciesRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax and on BioConda.     

Below-threshold mortality: implications for studies in evolution,ecology and demography

Evolutionary biologists, ecologists and experimental gerontologists have increasingly used estimates of age-specific mortality as a critical component in studies of a range of important biological processes. However, the analysis of age-specific mortality rates is plagued by specific statistical challenges caused by sampling error. Here we discuss the nature of this ‘demographic sampling error’, and the way in which it can bias our estimates of (1) rates of ageing, (2) age at onset of senescence, (3) costs of reproduction and (4) demographic tests of evolutionary models of ageing. We conducted simulations which suggest that using standard statistical techniques, we would need sample sizes on the order of tens of thousands in most experiments to effectively remove any bias due to sampling error. We argue that biologists should use much larger sample sizes than have previously been used. However, we also present simple maximum likelihood models that effectively remove biases due to demographic sampling error even at relatively small sample sizes.     

远缘杂交中不育基因的位置和效应的最大似然估计

提出了一种统计方法,利用标记位点的异常分离,来估计远缘杂交中不育基因位点的位置和效应,在回交群体中,用最大似然法对不育基因与标记位点之间的生组值和配子存活率进行估计。将表现连续分布的育性指标转化为百连续变异的遗传标记的分离,可以避免对育性直接观测所带来的重组值估计结果的不稳定,还可以同时估计雌雄配子的存活率。