Statistical Methodology Commons^™

A Markov Decision Process Approach To Optimal Control Of A Multi-Level Hierarchical Manpower System, Akaninyene U. Udom

CBN Journal of Applied Statistics (JAS)

A recurrent problem in manpower control is how to attain the desired structural configuration in an optimal way, since it is possible to reach a desired structural configuration using different control inputs. The major aim of this paper is to develop a Markov Decision Process for optimal control of a Multi-level Hierarchical Manpower System (MHMS) by promotion and interdepartmental transfers. This is examined under control by intervention and contraction cost Markov Decision Process.

Joint Estimation Of Multiple Graphical Models From High Dimensional Time Series, Huitong Qiu, Fang Han, Han Liu, Brian Caffo

Johns Hopkins University, Dept. of Biostatistics Working Papers

In this manuscript the problem of jointly estimating multiple graphical models in high dimensions is considered. It is assumed that the data are collected from n subjects, each of which consists of m non-independent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of the closeness between subjects. A kernel based method for jointly estimating all graphical models is proposed. Theoretically, under a double asymptotic framework, where both (m,n) and the dimension d can increase, the explicit rate of convergence in parameter estimation is provided, thus characterizing the strength one can borrow …

Adapting Data Adaptive Methods For Small, But High Dimensional Omic Data: Applications To Gwas/Ewas And More, Sara Kherad Pajouh, Alan E. Hubbard, Martyn T. Smith

U.C. Berkeley Division of Biostatistics Working Paper Series

Exploratory analysis of high dimensional "omics" data has received much attention since the explosion of high-throughput technology allows simultaneous screening of tens of thousands of characteristics (genomics, metabolomics, proteomics, adducts, etc., etc.). Part of this trend has been an increase in the dimension of exposure data in studies of environmental exposure and associated biomarkers. Though some of the general approaches, such as GWAS, are transferable, what has received less focus is 1) how to derive estimation of independent associations in the context of many competing causes, without resorting to a misspecified model, and 2) how to derive accurate small-sample inference …

Using The Inverse Transform To Specify Contrasts In Regression And Latent Curve Structural Equation Models, Thomas N. Templin

Nursing Faculty Research Publications

A simple yet general method for specifying contrasts to test hypotheses in regression and latent curve structural equation models is presented. The traditional qualitative variable coding schemes used in multiple regression (e.g., dummy coding) have a more general formulation. Five matrices are involved: The coding scheme, A. The matrix which gives the distribution and ordering of cases, W; WA = X; X is the design matrix. The contrast coefficient matrix C; and C^-1 = A. In practice, only C, C^-1, and A are necessary because the statistical software generates the design matrix. This method has great generality …

Testing For A Zero Proportion, Jonathan R. Bradley, David L. Farnsworth

Articles

Tests for a proportion that may be zero are described. The setting is an environment in which there can be misclassifications or misdiagnoses, giving the possibility of nonzero counts from false positives even though no real examples may exist. Both frequentist and Bayesian tests and analyses are presented, and examples are given.

Fast Covariance Estimation For High-Dimensional Functional Data, Luo Xiao, David Ruppert, Vadim Zipunnikov, Ciprian Crainiceanu

Johns Hopkins University, Dept. of Biostatistics Working Papers

For smoothing covariance functions, we propose two fast algorithms that scale linearly with the number of observations per function. Most available methods and software cannot smooth covariance matrices of dimension J x J with J>500; the recently introduced sandwich smoother is an exception, but it is not adapted to smooth covariance matrices of large dimensions such as J \ge 10,000. Covariance matrices of order J=10,000, and even J=100,000$ are becoming increasingly common, e.g., in 2- and 3-dimensional medical imaging and high-density wearable sensor data. We introduce two new algorithms that can handle very large covariance matrices: 1) FACE: a …

Trial Designs That Simultaneously Optimize The Population Enrolled And The Treatment Allocation Probabilities, Brandon S. Luber, Michael Rosenblum, Antoine Chambaz

Johns Hopkins University, Dept. of Biostatistics Working Papers

Standard randomized trials may have lower than desired power when the treatment effect is only strong in certain subpopulations. This may occur, for example, in populations with varying disease severities or when subpopulations carry distinct biomarkers and only those who are biomarker positive respond to treatment. To address such situations, we develop a new trial design that combines two types of preplanned rules for updating how the trial is conducted based on data accrued during the trial. The aim is a design with greater overall power and that can better determine subpopulation specific treatment effects, while maintaining strong control of …

Death Certificate Completion Skills Of Hospital Physicians In A Developing Country, Ahmed Suleman Haque, Kanza Shamim, Najm Hasan Siddiqui, Muhammad Irfan, Javaid Ahmed Khan

Section of Pulmonary & Critical Care

Background

Death certificates (DC) can provide valuable health status data regarding disease incidence, prevalence and mortality in a community. It can guide local health policy and help in setting priorities. Incomplete and inaccurate DC data, on the other hand, can significantly impair the precision of a national health information database. In this study we evaluated the accuracy of death certificates at a tertiary care teaching hospital in a Karachi, Pakistan.

Methods

A retrospective study conducted at Aga Khan University Hospital, Karachi, Pakistan for a period of six months. Medical records and death certificates of all patients who died under adult …

Ensemble-Based Methods For Forecasting Census In Hospital Units, Devin C. Koestler, Hernando Ombao, Jesse Bender

Dartmouth Scholarship

The ability to accurately forecast census counts in hospital departments has considerable implications for hospital resource allocation. In recent years several different methods have been proposed forecasting census counts, however many of these approaches do not use available patient-specific information. In this paper we present an ensemble-based methodology for forecasting the census under a framework that simultaneously incorporates both (i) arrival trends over time and (ii) patient-specific baseline and time-varying information. The proposed model for predicting census has three components, namely: current census count, number of daily arrivals and number of daily departures. To model the number of daily arrivals, …

Targeted Maximum Likelihood Estimation For Dynamic And Static Longitudinal Marginal Structural Working Models, Maya L. Petersen, Joshua Schwab, Susan Gruber, Nello Blaser, Michael Schomaker, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because …

Balancing Score Adjusted Targeted Minimum Loss-Based Estimation, Samuel D. Lendle, Bruce Fireman, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Adjusting for a balancing score is sufficient for bias reduction when estimating causal effects including the average treatment effect and effect among the treated. Estimators that adjust for the propensity score in a nonparametric way, such as matching on an estimate of the propensity score, can be consistent when the estimated propensity score is not consistent for the true propensity score but converges to some other balancing score. We call this property the balancing score property, and discuss a class of estimators that have this property. We introduce a targeted minimum loss-based estimator (TMLE) for a treatment specific mean with …

Optimal Tests Of Treatment Effects For The Overall Population And Two Subpopulations In Randomized Trials, Using Sparse Linear Programming, Michael Rosenblum, Han Liu, En-Hsu Yen

Johns Hopkins University, Dept. of Biostatistics Working Papers

We propose new, optimal methods for analyzing randomized trials, when it is suspected that treatment effects may differ in two predefined subpopulations. Such sub-populations could be defined by a biomarker or risk factor measured at baseline. The goal is to simultaneously learn which subpopulations benefit from an experimental treatment, while providing strong control of the familywise Type I error rate. We formalize this as a multiple testing problem and show it is computationally infeasible to solve using existing techniques. Our solution involves a novel approach, in which we first transform the original multiple testing problem into a large, sparse linear …

A Bayesian Regression Tree Approach To Identify The Effect Of Nanoparticles Properties On Toxicity Profiles, Cecile Low-Kam, Haiyuan Zhang, Zhaoxia Ji, Tian Xia, Jeffrey I. Zinc, Andre Nel, Donatello Telesca

COBRA Preprint Series

We introduce a Bayesian multiple regression tree model to characterize relationships between physico-chemical properties of nanoparticles and their in-vitro toxicity over multiple doses and times of exposure. Unlike conventional models that rely on data summaries, our model solves the low sample size issue and avoids arbitrary loss of information by combining all measurements from a general exposure experiment across doses, times of exposure, and replicates. The proposed technique integrates Bayesian trees for modeling threshold effects and interactions, and penalized B-splines for dose and time-response surfaces smoothing. The resulting posterior distribution is sampled via a Markov Chain Monte Carlo algorithm. This …

Analysis Of Spatial Data, Xiang Zhang

Theses and Dissertations--Statistics

In many areas of the agriculture, biological, physical and social sciences, spatial lattice data are becoming increasingly common. In addition, a large amount of lattice data shows not only visible spatial pattern but also temporal pattern (see, Zhu et al. 2005). An interesting problem is to develop a model to systematically model the relationship between the response variable and possible explanatory variable, while accounting for space and time effect simultaneously.

Spatial-temporal linear model and the corresponding likelihood-based statistical inference are important tools for the analysis of spatial-temporal lattice data. We propose a general asymptotic framework for spatial-temporal linear models and …

Income Inequality Measures And Statistical Properties Of Weighted Burr-Type And Related Distributions, Meznah R. Al Buqami

College of Graduate Studies: Theses & Dissertations

In this thesis, tail conditional expectation (TCE) in risk analysis, an important measure for right-tail risk, is presented. This value is generally based on the quantile of the loss distribution. Explicit formulas of several tail conditional expectations and inequality measures for Dagum-type models are derived. In addition, a new class of weighted Burr-III (WBIII) distribution is presented. The statistical properties of this distribution including hazard and reverse hazard functions, moments, coefficient of variation, skewness, and kurtosis, inequality measures, entropy are derived. Also, Fisher information and maximum likelihood estimates of the model parameters are obtained.

Group Testing Regression Models, Boan Zhang

Department of Statistics: Dissertations, Theses, and Student Research

Group testing, where groups of individual specimens are composited to test for the presence or absence of a disease (or some other binary characteristic), is a procedure commonly used to reduce the costs of screening a large number of individuals. Statistical research in group testing has traditionally focused on a homogeneous population, where individuals are assumed to have the same probability of having a disease. However, individuals often have different risks of positivity, so recent research has examined regression models that allow for heterogeneity among individuals within the population. This dissertation focuses on two problems involving group testing regression models. …

Quest For Continuous Improvement: Gathering Feedback And Data Through Multiple Methods To Evaluate And Improve A Library’S Discovery Tool, Jeanne M. Brown

Library Faculty Presentations

Summon at UNLV

• Implemented fall 2011: a web-scale discovery tool
• Expectations for Summon
• Continuous Summon Improvement (CSI)Group

The environment

• User changes
• Library changes
• Vendor changes
• Product changes
• Complex information environment
• Change + complexity = need to assess using multiple streams of feedback

Finding A Better Confidence Interval For A Single Regression Changepoint Using Different Bootstrap Confidence Interval Procedures, Bodhipaksha Thilakarathne

College of Graduate Studies: Theses & Dissertations

Recently a number of papers have been published in the area of regression changepoints but there is not much literature concerning confidence intervals for regression changepoints. The purpose of this paper is to find a better bootstrap confidence interval for a single regression changepoint. ("Better" confidence interval means having a minimum length and coverage probability which is close to a chosen significance level). Several methods will be used to find bootstrap confidence intervals. Among those methods a better confidence interval will be presented.

A Prior-Free Framework Of Coherent Inference And Its Derivation Of Simple Shrinkage Estimators, David R. Bickel

COBRA Preprint Series

The reasoning behind uses of confidence intervals and p-values in scientific practice may be made coherent by modeling the inferring statistician or scientist as an idealized intelligent agent. With other things equal, such an agent regards a hypothesis coinciding with a confidence interval of a higher confidence level as more certain than a hypothesis coinciding with a confidence interval of a lower confidence level. The agent uses different methods of confidence intervals conditional on what information is available. The coherence requirement means all levels of certainty of hypotheses about the parameter agree with the same distribution of certainty over parameter …

Investigation Of Trends And Predictive Effectiveness Of Crash Severity Models, James E. Mooradian

Master's Theses

This thesis describes analysis using ordinal logistic regression to uncover temporal patterns in the severity level (fatal, serious injury, minor injury, slight injury or no injury) for persons involved in highway crashes in Connecticut, focusing on the demographic split between senior travelers (65 years and over) and non-senior travelers. Existing state sources provide data describing the time and weather conditions for each crash and the vehicles and persons involved over the time period from 1995 to 2009 as well as the traffic volumes and the characteristics of the roads on which these crashes occurred. Findings indicate an overall increase in …