1,721,252 research outputs found
Empirical likelihood-based adjustment methods
Get PDFAuxiliary information is frequently used in survey sampling at the estimation stage to increase the precision of estimators. The class of calibration estimators introduced by Deville and Sarndal (1992) is obtained by replacing the design weights with the so-called calibration weights, i.e. the closest weights from the design weights (with respect to a given divergence measure) that satisfy the constraints that incorporate the auxiliary information. The presence of covariate information in experimental design situations is similar to the presence of auxiliary information in survey sampling. Methods for nonparametric covariance adjustment from Quade (1967), Puri and Sen (1971), Koch et al. (1982), and Koch et al. (1998) are similar to the calibration methods for the two survey samples described by Zieschang (1990). In all these cases the covariance-adjusted estimators are obtained from the unadjusted estimators by minimizing a quadratic criterion subject to equal means constraints on the covariates. Ideas from survey sampling are used to generalize previously mentioned methods for nonparametric covariance adjustment. First, an empirical likelihood-based adjustment method is proposed for the construction of confidence intervals for the difference between means. A stratified version of the method and related methods that use criteria based on other divergence measures are described. Next, empirical likelihood-based adjustment methods are developed for the difference between more general parameters of interest under more general constraints. Finally, alternative empirical likelihood-based methods, that use a weighted empirical likelihood criterion, are developed for the construction of confidence intervals for the difference between means and stratified versions
Functional Data Analytic Inference for Systems Governed By Differential Equations with Applications
Get PDFThe objective of this dissertation research is to develop formal statistical methodology for analyzing systems governed by ordinary differential equations (ODE). Ordinary differential equations are commonly used to describe a wide variety of biological and physiological phenomena. They arise in the description of gene regulatory networks, study of HIV dynamics and other infectious diseases and toxicology . This work is motivated by physiologically based pharmacokinetic (PBPK) models in toxicology which are deterministic models used to describe chemical kinetics in human or animal physiology. These models relate the concentration of chemicals in tissues and blood to their rates of change and physiological parameters, such as tissue volume and blood flow, and metabolic parameters among others, through a system of ODEs. Usual strategies of analyzing such models involve non-linear least squares methodology which can potentially be computationally intensive. Often, some of the existing procedures for modeling ODEs do not necessarily account for inter and intra-individual variability that are common in multi-subject experiments. Using functional data analytic methods, in this dissertation research, we provide a formal statistical framework for drawing statistical inferences regarding subject specific and population specific parameters in models governed by a system of ODE. One of the main features of the proposed methodology is to cast the problem in a constrained inferential framework and thus avoid solving the differential equations, which is often challenging and time consuming. Such a formulation allows for the possibility that all components of the ODE may not adequately describe the underlying biological phenomena. The proposed framework also allows the researcher to estimate both within and between subject variability, while drawing statistical inferences at the individual as well as the population level. We make as few assumptions as possible while taking into account the underlying structure in the data. The proposed framework allows researchers to compare parameters among several populations, such as different dose groups, while adjusting covariates, whether discrete or continuous. Such inferences were not possible until now. We illustrate the proposed methodology using some simulated data sets as well as a real data set on benzene concentration in exhaled breath
Multiple testing in genome-wide studies
Get PDFDNA microarray technologies allow us to monitor expression levels of thousands of genes simultaneously. A basic task in analyzing microarray data is the identification of differentially expressed genes under different experimental conditions. The null hypothsis is no association between the expression levels and explanatory variables or covariates. Family-wise error rate (FWER), although very conservative, controls type I error. False Discovery Rate (FDR) is a less stringent approach which aims to control the expected proportion of Type I errors among the rejected hypotheses. Since there are thousands of genes tested simultaneously, FDR may be enhanced. High correlation between tested genes, attributed to co-regulations and dependency in the measurement errors, further complicates the problem. Most of the current FDR procedures assume independence or rather restrictive dependence structures, resulting in being less reliable. In this work, we address these very large multiplicity problems by adopting a two-stage FDR controlling procedure under suitable dependence structures and based on Poisson distributional approximation, which eliminates the need to assume restricted dependence structures. We compare the performance of the proposed FDR procedure with that of other FDR controlling procedures, with illustration of the leukemia microarray study of Golub et al. (1999) and simulated data. In these studies, the proposed FDR procedure has greater power without much elevation of FDR. Current FDR procedures have not been used extensively in genomic sequences involving count or discrete, or purely qualitative responses, confronted with high-dimensional low sample size constraints. Using the 2002-03 SARS epidemic model, it is shown that proposed FDR procedure along with an appropriate test statistic based on a pseudo-marginal approach with Hamming distance performs better. Finally, for classfication of genes of dependent genes with heterogeneity amidst a small sample, standard robust inference may not work out. This issue involves setting up a hypothesis when parameters of interest are subject to inequality restrictions. Usual (restricted) likelihood based statistical inference procedures may not be computationally intensive. Roy's union-intersection principle may be a viable alternative. The breast cancer study of Lobenhofer et al. is included for numerical illustration
Statistical theory and robust methodology for nonlinear models with application to toxicology
Get PDFNonlinear regression models are commonly used in dose-response studies, especially when researchers are interested in determining various toxicity characteristics of a chemical or a drug. There are several issues one needs to pay attention to when fitting nonlinear models for toxicology data, such as structure for the error variance in the model and the presence of potential influential and outlying observations. In this dissertation I developed robust statistical methods for analyzing nonlinear regression models, which are based on robust M-estimation and preliminary test estimation (PTE) procedures. In the first part of this research the M-estimation methods in heteroscedastic nonlinear models are considered for two cases. In one case, the error variance is proportional to some known function of mean response, while in the other case the error variance is modeled as a polynomial function of dose. The asymptotic properties of the proposed M-procedures and the asymptotic efficiency of the proposed M-estimators are provided. In the second part I consider PTE-based methodology using M-methods for estimating the regression parameters. Based on the outcome of the preliminary test, the proposed methodology determines the appropriate error variance structure for the data and accordingly chooses the suitable estimation procedure. Since the resulting methodology uses M-estimators, it is expected to be robust to outliers and influential observations, although such issues have not been explored in this dissertation. Consequently, one does not have to pre-specify the error structure for the variances not does the user have to perform model diagnostics to choose a method of estimation. Some asymptotic results will be given to obtain the asymptotic covariance matrix of the PTE. Finally numerical studies are presented to illustrate the methodology. The results of the numerical studies suggest that the PTE using M-methods performs well and is robust to the error variance structure
Bayesian Model Based Approaches In The Analysis Of Chromatin Structure And Motif Discovery
Get PDFEfficient detection of transcription factor (TF) binding sites is an important and unsolved problem in computational genomics. Recently, due to the poor predictive ability of motif finding algorithms, along with the recent proliferation of high-throughput genomic technologies, there has been a drive to utilize secondary information, such as the positioning of nucleosomes, for improving predictions. Nucleosomes prevent transcription factor binding at those sites by blocking the TF access to the DNA. We aimed to construct an accurate map of nucleosome-free regions (NFRs), based on data from high-throughput genomic tiling arrays in yeast. Direct use of Hidden Markov Models are not always applicable due to variable-sized gaps and missing data. So we have extended the hidden Markov model procedure to a continuous time version while efficiently incorporating DNA sequence features that are relevant to nucleosome formation. Simulation studies and an application to a yeast nucleosomal assay demonstrate the advantages of the new method. The established biological role of nucleosomes in relation to TF binding, led us to formulate a joint model in the fourth chapter. The algorithm was implemented on the FAIRE data set, and comparisons were made with existing motif search algorithms. The fifth chapter deals with HMM asymptotics. We obtained results on consistency asymptotic normality and contiguity of a hidden Markov model. These have helped our inference on the convergence properties of the posterior and the consistency of the Bayesian posterior estimates. This has led to the conclusion that the Bayesian inference of a HMM run on sufficiently large datasets (which is typical, in the case of genomic data) leads us very close to the underlying true parameters, as in the case of iid models. The result is fairly general in nature to provide the justification for HMM inference in a wide variety of datasets
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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