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Fully Exponential Laplace Approximation Em Algorithm For Nonlinear Mixed Effects Models, Meijian Zhou

Department of Statistics: Dissertations, Theses, and Student Research

Nonlinear mixed effects models provide a flexible and powerful platform for the analysis of clustered data that arise in numerous fields, such as pharmacology, biology, agriculture, forestry, and economics. This dissertation focuses on fitting parametric nonlinear mixed effects models with single- and multi-level random effects. A new, efficient, and accurate method that gives an error of order O(1/n²), fully exponential Laplace approximation EM algorithm (FELA-EM), for obtaining restricted maximum likelihood (REML) estimates in nonlinear mixed effects models is developed. Sample codes for implementing FELA-EM algorithm in R are given. Simulation studies have been conducted to evaluate …

Forced Oscillations Of The Korteweg-De Vries Equation On A Bounded Domain And Their Stability, Muhammad Usman, Bingyu Zhang

Mathematics Faculty Publications

It has been observed in laboratory experiments that when nonlinear dispersive waves are forced periodically from one end of undisturbed stretch of the medium of propagation, the signal eventually becomes temporally periodic at each spatial point. The observation has been confirmed mathematically in the context of the damped Kortewg-de Vries (KdV) equation and the damped Benjamin-Bona-Mahony (BBM) equation. In this paper we intend to show the same results hold for the pure KdV equation (without the damping terms) posed on a bounded domain. Consideration is given to the initial-boundary-value problem

uu_xu_xxx 0 < x < 1, t > 0, (*)

It is shown …

Kendall's Tau And Spearman's Rho For Zero-Inflated Data, Ronald Silva Pimentel

Dissertations

Zero-inflated continuous distributions have positive probability mass at zero in addition to a continuous distribution. Such type of data can be encountered, for example, in medical, environmental and financial research. The main focus of this research is to study the association of nonnegative random variables, both having a positive probability mass at zero. New estimators of the classical measures of association, Kendall's tau and Spearman's rho, appropriate for the zero-inflated distributions, are proposed and their asymptotic distributions are derived. Performance of the estimators is assessed by a Monte Carlo simulation study. New ideas are illustrated by a real data example.

A Two-Sample Adaptive Procedure Based On The Log-Rank And Peto And Peto's Wilcoxon Tests, Annie A. Tordilla

Dissertations

It has been shown that under a location-scale model y = μ + βz + σε at where y is right censored, the Log-Rank test is asymptotically efficient for the Extreme minimum value error distribution while Peto and Peto's Wilcoxon test is asymptotically efficient for the Logistic error distribution. We propose a two-sample adaptive test, which first selects between Extreme minimum value and Logistic error distribution as to which is a better fit to the data, then performs the asymptotically efficient test (Log-Rank or Peto and Peto's Wilcoxon test) for the selected distribution. The performance of the adaptive test is …

Metaevaluation Of Hiv/Aids Prevention Intervention Evaluations In Sub Saharan Africa With A Specific Emphasis On Implications For Women And Girls, Tererai Mafukidze Trent

Dissertations

Despite numerous attempts by international agencies to halt the spread of Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS), nowhere has the impact of HIV/AIDS been felt more acutely than among women and girls in Sub-Saharan Africa (SSA). SSA women account for 59% of adults over the age of 15 living with HIV/AIDS and 76% of those 15-24 who are infected (United Nations Joint Programme on HIV/AIDS [UNAIDS], 2007).

The evidence on gender disparities in infection rates is indisputable; there is an urgent need to identify what is missing in HIV/AIDS prevention interventions: What is the evidence based upon …

A Practical Solution To The Reference Class Problem, Edward K. Cheng

Vanderbilt Law School Faculty Publications

The "reference class problem" is a serious challenge to the use of statistical evidence that arguably arises every day in wide variety of cases, including toxic torts, property valuation, and even drug smuggling. At its core, it observes that statistical inferences depend critically on how people, events, or things are classified. As there is (purportedly) no principle for privileging certain categories over others, statistics become manipulable, undermining the very objectivity and certainty that make statistical evidence valuable and attractive to legal actors. In this paper, I propose a practical solution to the reference class problem by drawing on model selection …

Statistical Analysis Of Linear Analog Circuits Using Gaussian Message Passing In Factor Graphs, Miti Phadnis

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

This thesis introduces a novel application of factor graphs to the domain of analog circuits. It proposes a technique of leveraging factor graphs for performing statistical yield analysis of analog circuits that is much faster than the standard Monte Carlo/Simulation Program With Integrated Circuit Emphasis (SPICE) simulation techniques. We have designed a tool chain to model an analog circuit and its corresponding factor graph and then use a Gaussian message passing approach along the edges of the graph for yield calculation. The tool is also capable of estimating unknown parameters of the circuit given known output statistics through backward message …

On The Testing And Estimation Of High-Dimensional Covariance Matrices, Thomas Fisher

All Dissertations

Many applications of modern science involve a large number of parameters. In
many cases, the number of parameters, p, exceeds the number of observations,
N. Classical multivariate statistics are based on the assumption that the
number of parameters is fixed and the number of observations is large. Many of
the classical techniques perform poorly, or are degenerate, in high-dimensional
situations.
In this work, we discuss and develop statistical methods for inference of
data in which the number of parameters exceeds the number of observations.
Specifically we look at the problems of hypothesis testing regarding and the
estimation of the covariance …

Risk Matrix Input Data Biases, Eric D. Smith, William T. Siefert, David Drain

Engineering Management and Systems Engineering Faculty Research & Creative Works

Risk matrices used in industry characterize particular risks in terms of the likelihood of occurrence, and the consequence of the actualized risk. Human cognitive bias research led by Daniel Kahneman and Amos Tversky exposed systematic translations of objective probability and value as judged by human subjects. Applying these translations to the risk matrix allows the formation of statistical hypotheses of risk point placement biases. Industry-generated risk matrix data reveals evidence of biases in the judgment of likelihood and consequence-principally, likelihood centering, a systematic increase in consequence, and a diagonal bias. Statistical analyses are conducted with linear regression, normal distribution fitting, …

Pragmatic Estimation Of A Spatio-Temporal Air Quality Model With Irregular Monitoring Data, Paul D. Sampson, Adam A. Szpiro, Lianne Sheppard, Johan Lindström, Joel D. Kaufman

UW Biostatistics Working Paper Series

Statistical analyses of the health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in “land-use” regression models. More recently these regression models have accounted for spatial correlation structure in combining monitoring data with land-use covariates. The current paper builds on these concepts to address spatio-temporal prediction of ambient concentrations of particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) on the basis of a model representing spatially varying seasonal trends and spatial correlation structures. Our hierarchical methodology provides a pragmatic approach that fully exploits regulatory and other supplemental monitoring data which jointly …

On The Behaviour Of Marginal And Conditional Akaike Information Criteria In Linear Mixed Models, Sonja Greven, Thomas Kneib

Johns Hopkins University, Dept. of Biostatistics Working Papers

In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion (AIC) have been used, based either on the marginal or on the conditional distribution. We show that the marginal AIC is no longer an asymptotically unbiased estimator of the Akaike information, and in fact favours smaller models without random effects. For the conditional AIC, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that leads to the selection of any random effect not predicted to be exactly zero. We derive …

Survival Analysis With Error-Prone Time-Varying Covariates: A Risk Set Calibration Approach, Xiaomei Liao, David M. Zucker, Yi Li, Donna Spiegelman

Harvard University Biostatistics Working Paper Series

No abstract provided.

Is Survival The Only Or Even The Right Outcome For Evaluating Treatments For Out-Of-Hospital Cardiac Arrest? A Proposed Test Based On Both An Intermediate And Ultimate Outcome., Al Hallstrom

UW Biostatistics Working Paper Series

It is generally agreed that the goal of resuscitation is survival with neurological and physiological status similar to that preceding the cardiac arrest. Previously I have argued that the lack of improvement in outcome from resuscitation over the past 3 to 4 decades, as compared to the substantial progress made in treatment of ischemic heart disease, is a consequence of the absence of randomized clinical trials of new interventions and the use of intermediate endpoints such as return of spontaneous circulation or admittance to hospital. Proponents of these intermediate endpoints have argued that those involved in the resuscitation have no …

A New Class Of Minimum Power Divergence Estimators With Applications To Cancer Surveillance, Nirian Martin, Yi Li

Harvard University Biostatistics Working Paper Series

No abstract provided.

Nonlinear Models In Multivariate Population Bioequivalence Testing, Bassam Dahman

Theses and Dissertations

In this dissertation a methodology is proposed for simultaneously evaluating the population bioequivalence (PBE) of a generic drug to a pre-licensed drug, or the bioequivalence of two formulations of a drug using multiple correlated pharmacokinetic metrics. The univariate criterion that is accepted by the food and drug administration (FDA) for testing population bioequivalence is generalized. Very few approaches for testing multivariate extensions of PBE have appeared in the literature. One method uses the trace of the covariance matrix as a measure of total variability, and another uses a pooled variance instead of the reference variance. The former ignores the correlation …

Two-Stage Decompositions For The Analysis Of Functional Connectivity For Fmri With Application To Alzheimer's Disease Risk, Brian S. Caffo, Ciprian M. Crainiceanu, Guillermo Verduzco, Stewart H. Mostofsky, Susan Spear-Bassett, James J. Pekar

COBRA Preprint Series

Functional connectivity is the study of correlations in measured neurophysiological signals. Altered functional connectivity has been shown to be associated with numerous diseases including Alzheimer's disease and mild cognitive impairment. In this manuscript we use a two-stage application of the singular value decomposition to obtain data driven population-level measures of functional connectivity in functional magnetic resonance imaging (fMRI). The method is computationally simple and amenable to high dimensional fMRI data with large numbers of subjects. Simulation studies suggest the ability of the decomposition methods to recover population brain networks and their associated loadings. We further demonstrate the utility of these …

Parameter Estimation For The Lognormal Distribution, Brenda Faith Ginos

Theses and Dissertations

The lognormal distribution is useful in modeling continuous random variables which are greater than or equal to zero. Example scenarios in which the lognormal distribution is used include, among many others: in medicine, latent periods of infectious diseases; in environmental science, the distribution of particles, chemicals, and organisms in the environment; in linguistics, the number of letters per word and the number of words per sentence; and in economics, age of marriage, farm size, and income. The lognormal distribution is also useful in modeling data which would be considered normally distributed except for the fact that it may be more …

Analyzing Bivariate Survival Data With Interval Sampling And Application To Cancer Epidemiology, Hong Zhu, Mei-Cheng Wang

Johns Hopkins University, Dept. of Biostatistics Working Papers

In medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. In cancer studies interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme where the first failure event (cancer onset) is identified within a calendar time interval, the time of the initiating event (birth) can be retrospectively confirmed, and the occurrence of the second event (death) is observed sub ject to right censoring. To …

Joint Mixed-Effects Models For Longitudinal Data Analysis: An Application For The Metabolic Syndrome, John Thorp Iii

Theses and Dissertations

Mixed-effects models are commonly used to model longitudinal data as they can appropriately account for within and between subject sources of variability. Univariate mixed effect modeling strategies are well developed for a single outcome (response) variable that may be continuous (e.g. Gaussian) or categorical (e.g. binary, Poisson) in nature. Only recently have extensions been discussed for jointly modeling multiple outcome variables measures longitudinally. Many diseases processes are a function of several factors that are correlated. For example, the metabolic syndrome, a constellation of cardiovascular risk factors associated with an increased risk of cardiovascular disease and type 2 diabetes, is often …

A Comparison Of Frequentist And Bayesian Approaches To The Estimation Of Long-Stay Per-Diems, Jeff Hatcher, Jason M. Sutherland

Dartmouth Scholarship

Within many diagnosis related group (DRG) systems, there is recognition that a single cost weight per DRG is not suitable, and that cost weights should take into account extremely lengthy hospital stays. Long lengths of stay are considered to be due to factors largely beyond the control of the hospital, and a single weight per DRG would potentially place hospitals under financial risk.

Within Canada's acute-care, inpatient grouping methodology - Case Mix Groups (CMG+) - long-stay episodes represent approximately 4.5% of all discharges. Within a CMG (analogous to DRG), the cost weight assigned to long-stay cases consists of the typical …

Lot Quality Assurance Sampling (Lqas) And The Mozambique Malaria Indicator Surveys, Caitlin Biedron, Marcello Pagano, Bethany L. Hedt, Albert Kilian, Amy Ratcliffe, Samuel Mabunda, Joseph J. Valadez

Harvard University Biostatistics Working Paper Series

No abstract provided.

Modeling Multilevel Sleep Transitional Data Via Poisson Log-Linear Multilevel Models, Bruce J. Swihart, Brian Caffo, Ciprian Crainiceanu, Naresh M. Punjabi

Johns Hopkins University, Dept. of Biostatistics Working Papers

This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified …

Bayesian Functional Data Analysis Using Winbugs, Ciprian M. Crainiceanu, A. Jeffrey Goldsmith

Johns Hopkins University, Dept. of Biostatistics Working Papers

We provide user friendly software for Bayesian analysis of Functional Data Models using WinBUGS 1.4. The excellent properties of Bayesian analysis in this context are due to: 1) dimensionality reduction, which leads to low dimensional projection bases; 2)the mixed model representation of functional models, which provides a modular approach to model extension; and 3) the orthogonality of the principal component bases, which contributes to excellent chain convergence and mixing properties. Our paper provides one more, essential, reason for using Bayesian analysis for Functional models: the existence of software.

Application Of The Truncated Skew Laplace Probability Distribution In Maintenance System, Gokarna R. Aryal, Chris P. Tsokos

Journal of Modern Applied Statistical Methods

A random variable X is said to have the skew-Laplace probability distribution if its pdf is given by f(x) = 2g(x)G(λx), where g (.) and G (.), respectively, denote the pdf and the cdf of the Laplace distribution. When the skew Laplace distribution is truncated on the left at 0 it is called it the truncated skew Laplace (TSL) distribution. This article provides a comparison of TSL distribution with twoparameter gamma model and the hypoexponential model, and an application of the subject model in maintenance system is studied.

Examples Of Computing Power For Zero-Inflated And Overdispersed Count Data, Suzanne R. Doyle

Journal of Modern Applied Statistical Methods

Examples of zero-inflated Poisson and negative binomial regression models were used to demonstrate conditional power estimation, utilizing the method of an expanded data set derived from probability weights based on assumed regression parameter values. SAS code is provided to calculate power for models with a binary or continuous covariate associated with zero-inflation.

An Inductive Approach To Calculate The Mle For The Double Exponential Distribution, W. J. Hurley

Journal of Modern Applied Statistical Methods

Norton (1984) presented a calculation of the MLE for the parameter of the double exponential distribution based on the calculus. An inductive approach is presented here.

New Effect Size Rules Of Thumb, Shlomo S. Sawilowsky

Journal of Modern Applied Statistical Methods

Recommendations to expand Cohen’s (1988) rules of thumb for interpreting effect sizes are given to include very small, very large, and huge effect sizes. The reasons for the expansion, and implications for designing Monte Carlo studies, are discussed.

Generating And Comparing Aggregate Variables For Use Across Datasets In Multilevel Analysis, James Chowhan, Laura Duncan

Journal of Modern Applied Statistical Methods

This article examines the creation of contextual aggregate variables from one dataset for use with another dataset in multilevel analysis. The process of generating aggregate variables and methods of assessing the validity of the constructed aggregates are presented, together with the difficulties that this approach presents.

Detecting Lag-One Autocorrelation In Interrupted Time Series Experiments With Small Datasets, Clare Riviello, S. Natasha Beretvas

Journal of Modern Applied Statistical Methods

The power and type I error rates of eight indices for lag-one autocorrelation detection were assessed for interrupted time series experiments (ITSEs) with small numbers of data points. Performance of Huitema and McKean’s (2000) z_HM statistic was modified and compared with the z_HM, five information criteria and the Durbin-Watson statistic.

Relationship Between Internal Consistency And Goodness Of Fit Maximum Likelihood Factor Analysis With Varimax Rotation, Gibbs Y. Kanyongo, James B. Schreiber

Journal of Modern Applied Statistical Methods

This study investigates how reliability (internal consistency) affects model-fitting in maximum likelihood exploratory factor analysis (EFA). This was accomplished through an examination of goodness of fit indices between the population and the sample matrices. Monte Carlo simulations were performed to create pseudo-populations with known parameters. Results indicated that the higher the internal consistency the worse the fit. It is postulated that the observations are similar to those from structural equation modeling where a good fit with low correlations can be observed and also the reverse with higher item correlations.