Statistics and Probability Commons^™

Heterogeneity, Control, Social Infrastructure, And Possibilities Of Participation: Their Interplay In Modern Understandings Of Heredity And In Interpretation Of Science, Peter J. Taylor

Working Papers on Science in a Changing World

This working paper is a prospectus for research, writing, and engagement. It consists of vignettes, sketches of lines of inquiry, and proposals for engagement, all of which concern modern understandings of heredity and development over the life course as well as the social interpretation of science. The various items address a range of areas of science and of its interpretation: heritability studies, the social uses of genetic information, gene-by-environment interaction, personalized medicine, IQ paradoxes, racial group membership, biobanks, and life events and difficulties research. Fresh perspectives in these areas are opened up by examining the ways that research and application …

Book Review: What’S Luck Got To Do With It? The History, Mathematics, And Psychology Of The Gambler’S Illusion By Joseph Mazur, Michael Lugo

Journal of Humanistic Mathematics

This review of Joseph Mazur's book on the history of gambling, for a general audience, is in three parts, paralleling the structure of the book. The first part briefly outlines Mazur's coverage of the history of probability from prehistory to the present day, with a focus on gambling. The second part examines the relationship between the mathematics of gambling and probability theory, and summarizes classical problems in probability arising from gambling such as Galileo's dice and the Pascal-Fermat problem of points. The third part, on psychology, discusses the gambler's illusion and psychological motivations for gambling.

Rumors Of Our Rarity Are Greatly Exaggerated: Bad Statistics About Women In Science, Cathy Kessel

Journal of Humanistic Mathematics

During the past few years, three bad statistics have been persistently used in discussions of women in science, technology, engineering, and mathematics (STEM). The first was questionable when it was published in 1983 and has since been widely used. The second came to prominence in 2006 – and now leads an international and perhaps eternal life on the Web. The third may have made its debut in 2007. Its variants occur in popular and academic books and journals, including the 2011 Proceedings of the National Academy of Sciences.

This report presents case histories of the three bad statistics, suggests writing …

A Parallel Robin-Robin Domain Decomposition Method For The Stokes-Darcy System, Wenbin Chen, Max Gunzburger, Fei Hua, Xiaoming Wang

Mathematics and Statistics Faculty Research & Creative Works

We propose a new parallel Robin-Robin domain decomposition method for the coupled Stokes-Darcy system with Beavers-Joseph-Saffman-Jones interface boundary condition. in particular, we prove that, with an appropriate choice of parameters, the scheme converges geometrically independent of the mesh size. © 2011 Society for Industrial and Applied Mathematics.

Targeted Maximum Likelihood Estimation Of Natural Direct Effect, Wenjing Zheng, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In many causal inference problems, one is interested in the direct causal effect of an exposure on an outcome of interest that is not mediated by certain intermediate variables. Robins and Greenland (1992) and Pearl (2000) formalized the definition of two types of direct effects (natural and controlled) under the counterfactual framework. Since then, identifiability conditions for these effects have been studied extensively. By contrast, considerably fewer efforts have been invested in the estimation problem of the natural direct effect. In this article, we propose a semiparametric efficient, multiply robust estimator for the natural direct effect of a binary treatment …

On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

General Recognition Theory Extended To Include Response Times: Predictions For A Class Of Parallel Systems, Joseph W. Houpt, James T. Townsend, Noah H. Silbert

Psychology Faculty Publications

No abstract provided.

A Statistical Test For The Capacity Coefficient, Joseph W. Houpt, James T. Townsend

Psychology Faculty Publications

No abstract provided.

From Deep Space 9 To The Gamma Quadrant!, James T. Townsend, Joseph W. Houpt

Psychology Faculty Publications

No abstract provided.

Targeted Minimum Loss Based Estimation Based On Directly Solving The Efficient Influence Curve Equation, Paul Chaffee, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Applying targeted maximum likelihood estimation to longitudinal data can be computationally intensive. As the number of time points and/or number of intermediate factors grows, the computation resources consumed by these algorithms likewise increases. Different TMLE algorithms have different computational speeds and implementation challenges; there may also be efficiency differences of the corresponding estimators. The algorithm we describe here proceeds by solving the empirical efficient influence curve equation directly using numerical computation methods, rather than indirectly (by solving a score equation), which is the usual route. We believe that this estimator is the simplest of the TMLE procedures to implement in …

Assessing Medicare Beneficiaries’ Strength‐Of‐Preference Scores For Health Care Options: How Engaging Does The Elicitation Technique Need To Be?, Trafford Crump, Hilary A. Llewellyn-Thomas

Dartmouth Scholarship

The objective was to determine if participants’ strength‐of‐preference scores for elective health care interventions at the end‐of‐life (EOL) elicited using a non‐engaging technique are affected by their prior use of an engaging elicitation technique.

Computational Insight With Monte Carlo Simulations, Boyan Kostadinov

Publications and Research

We introduce Monte Carlo simulations for estimating areas by playing a game of "darts". We also introduce simulations of random walks. We use compact, vectorized programming, based on the R language, for all computer simulations and visualizations, aimed at high school students. This presentation is based on the Invited, prime time lecture given at the summer camp for gifted high school students at City College of New York, July 13, 2011.

Variable Importance Analysis With The Multipim R Package, Stephan J. Ritter, Nicholas P. Jewell, Alan E. Hubbard

U.C. Berkeley Division of Biostatistics Working Paper Series

We describe the R package multiPIM, including statistical background, functionality and user options. The package is for variable importance analysis, and is meant primarily for analyzing data from exploratory epidemiological studies, though it could certainly be applied in other areas as well. The approach taken to variable importance comes from the causal inference field, and is different from approaches taken in other R packages. By default, multiPIM uses a double robust targeted maximum likelihood estimator (TMLE) of a parameter akin to the attributable risk. Several regression methods/machine learning algorithms are available for estimating the nuisance parameters of the models, including …

Reduced Bayesian Hierarchical Models: Estimating Health Effects Of Simultaneous Exposure To Multiple Pollutants, Jennifer F. Bobb, Francesca Dominici, Roger D. Peng

Johns Hopkins University, Dept. of Biostatistics Working Papers

Quantifying the health effects associated with simultaneous exposure to many air pollutants is now a research priority of the US EPA. Bayesian hierarchical models (BHM) have been extensively used in multisite time series studies of air pollution and health to estimate health effects of a single pollutant adjusted for potential confounding of other pollutants and other time-varying factors. However, when the scientific goal is to estimate the impacts of many pollutants jointly, a straightforward application of BHM is challenged by the need to specify a random-effect distribution on a high-dimensional vector of nuisance parameters, which often do not have an …

A Unified Approach To Non-Negative Matrix Factorization And Probabilistic Latent Semantic Indexing, Karthik Devarajan, Guoli Wang, Nader Ebrahimi

COBRA Preprint Series

Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two matrices, W and H, each with nonnegative entries, V ~ WH. NMF has been shown to have a unique parts-based, sparse representation of the data. The nonnegativity constraints in NMF allow only additive combinations of the data which enables it to learn parts that have distinct physical representations in reality. In the last few years, NMF has been successfully applied in a variety of areas such as natural language processing, information retrieval, image processing, speech recognition …

Multiple Testing Of Local Maxima For Detection Of Unimodal Peaks In 1d, Armin Schwartzman, Yulia Gavrilov, Robert J. Adler

Harvard University Biostatistics Working Paper Series

No abstract provided.

Targeted Methods For Finding Quantitative Trait Loci, Hui Wang, Sherri Rose, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Conventional genetic mapping methods typically assume parametric models with Gaussian errors, and obtain parameter estimates through maximum likelihood estimation. We propose a general semiparametric model to map quantitative trait loci (QTL) in experimental crosses. In contrast with widely-used interval mapping (IM) derived methods, our model requires fewer assumptions and also accommodates various machine learning algorithms. Estimation using both targeted maximum likelihood and collaborative targeted maximum likelihood methods is compared to a composite interval mapping (CIM) approach. We demonstrate with simulations and real data analyses that, on average, our semiparametric targeted learning approach produces less biased QTL effect estimates than those …

Assessing The Effect Of Wal-Mart In Rural Utah Areas, Angela Nelson

Theses and Dissertations

Walmart and other “big box” stores seek to expand in rural markets, possibly due to cheap land and lack of zoning laws. In August 2000, Walmart opened a store in Ephraim, a small rural town in central Utah. It is of interest to understand how Walmart's entrance into the local market changes the sales tax revenue base for Ephraim and for the surrounding municipalities. It is thought that small “Mom and Pop” stores go out of business because they cannot compete with Walmart's prices, leading to a decrease in variety, selection, convenience, and most importantly, sales tax revenue base in …

An Introduction To Bayesian Methodology Via Winbugs And Proc Mcmc, Heidi Lula Lindsey

Theses and Dissertations

Bayesian statistical methods have long been computationally out of reach because the analysis often requires integration of high-dimensional functions. Recent advancements in computational tools to apply Markov Chain Monte Carlo (MCMC) methods are making Bayesian data analysis accessible for all statisticians. Two such computer tools are Win-BUGS and SASR 9.2's PROC MCMC. Bayesian methodology will be introduced through discussion of fourteen statistical examples with code and computer output to demonstrate the power of these computational tools in a wide variety of settings.

Gene Set Analysis For Longitudinal Gene Expression Data, Ke Zhang, Haiyan Wang, Arne C. Bathke, Solomon W. Harrar, Hans-Peter Piepho, Youping Deng

Statistics Faculty Publications

BACKGROUND: Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations. GSA performs statistical tests for independent microarray samples at the level of gene sets rather than individual genes. Nowadays, an increasing number of microarray studies are conducted to explore the dynamic changes of gene expression in a variety of species and biological scenarios. In these longitudinal studies, gene expression is repeatedly measured over time such that a GSA needs to take into account the within-gene correlations in addition to possible between-gene correlations.

RESULTS: We provide …