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C-SHIFT, Quantile Theory, and Assessing Monotonicity
Get PDFDNA microarray technology is a powerful tool for analyzing patterns in gene expression data for thousands of genes. Due to a number of systematic variations in microarray experiments, the raw gene expression data is often obfuscated by undesirable technical noises. Various normalization techniques were designed in an attempt to remove these non-biological errors prior to any statistical analysis. One of the reasons for normalizing data is the need for recovering the covariance matrix used in gene network analysis. We introduce and demonstrate a novel normalization technique, called the covariance shift (C-SHIFT) method. We prove that under certain conditions applying quantile normalization prior to performing Welch’s t-test can increase the value of the test statistics. We discuss the probabilistic monotonicity property of covariance graph models through a set of mean and correlation inequalities. Our analysis suggests that for two different studies, comprising healthy and cervical cancer patients, underlying biological networks largely follow the probabilistic monotonicity property
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Positive graphical lasso estimation of sparse inverse covariance matrices
Get PDFWe explore the possibility of estimating sparse inverse covariance matrices when for scientific reasons the covariance matrix is restricted to be a non-negative matrix. The process mirrors the graphical lasso process developed by Friedman and others(2008) that did not have this additional constraint.Accordingly, the Lasso procedure is done through coordinate descent. To easily add the constraint, we modified the LARS function created by Efron and others(2004) to perform positive Lasso (pLasso) estimation. The process is demonstrated on several time series generated datasets to clearly show the effectiveness and limitations
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
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maRkov : Goodness-of-fit Tests for Binary Markov Chains
Get PDFMarkov chains, discovered over one hundred years ago by Andrey Markov in an
attempt to analyze the distribution of vowels and consonants in Russian poetry, have
evolved in multiple fields over the last century to become a mainstay of statistical
modeling and computation. maRkov is a package of software to be deployed within the
R programming environment that allows for various tests to be performed to check
the appropriateness of different Markov Chain models for sets of observed binary
data.
Key Words: Markov chains, LRT, binary data, backward forward algorithm,
exchangeable sequenc
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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Matrix-Free Methods in Spatial-Temporal Modeling
No full textSpatial-temporal data arises in many applications, for example, environment sciences and disease mapping. This dissertation focuses on Gaussian spatial-temporal data. To make statistical inference for Gaussian spatial-temporal data, we developed a special class of spatial-temporal Gaussian state-space models in which the state vectors are constructed following spatial-temporal Gaussian autoregressions that are consistent with the conditional formulation of auto-normal spatial fields. We then proposed a matrix-free h-likelihood method to predict state vectors and to estimate parameters. The proposed method provides the same inference as that obtained from the Kalman filter and residual maximum likelihood analysis. However, for data from a large number of spatial sites, it is shown to have significant computational advantages. The novel elements of the dissertation include how we present a spatial-temporal state-space model as a linear regression model and develop estimation by matrix-free computations. Furthermore, the dissertation details inference in small time steps, indicates how the proposed method can be adapted to other complex spatial-temporal dynamical models based on stochastic partial differential equations, and discusses matrix-free Bayesian computations for non-Gaussian spatial data modeling. The method applies to data with both regularly and irregularly sampled spatial locations, and is illustrated through a simulation study and two data examples, one with monthly soil moistures across North America and the other with atmospheric concentrations of total nitrate across Eastern North America
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