Digital Commons Network^™

Semiparametric Maximum Likelihood Estimation In Normal Transformation Models For Bivariate Survival Data, Yi Li, Ross L. Prentice, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Accounting For Errors From Predicting Exposures In Environmental Epidemiology And Environmental Statistics, Adam A. Szpiro, Lianne Sheppard, Thomas Lumley

UW Biostatistics Working Paper Series

PLEASE NOTE THAT AN UPDATED VERSION OF THIS RESEARCH IS AVAILABLE AS WORKING PAPER 350 IN THE UNIVERSITY OF WASHINGTON BIOSTATISTICS WORKING PAPER SERIES (http://www.bepress.com/uwbiostat/paper350).

In environmental epidemiology and related problems in environmental statistics, it is typically not practical to directly measure the exposure for each subject. Environmental monitoring is employed with a statistical model to assign exposures to individuals. The result is a form of exposure misspecification that can result in complicated errors in the health effect estimates if the exposure is naively treated as known. The exposure error is neither “classical” nor “Berkson”, so standard regression calibration methods …

A Naive, Robust And Stable State Estimate, Todd Gordon Remund

Theses and Dissertations

A naive approach to filtering for feedback control of dynamic systems that is robust and stable is proposed. Simulations are run on the filters presented to investigate the robustness properties of each filter. Each simulation with the comparison of the filters is carried out using the usual mean squared error. The filters to be included are the classic Kalman filter, Krein space Kalman, two adjustments to the Krein filter with input modeling and a second uncertainty parameter, a newly developed filter called the Naive filter, bias corrected Naive, exponentially weighted moving average (EWMA) Naive, and bias corrected EWMA Naive filter.

Supervised Distance Matrices: Theory And Applications To Genomics, Katherine S. Pollard, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We propose a new approach to studying the relationship between a very high dimensional random variable and an outcome. Our method is based on a novel concept, the supervised distance matrix, which quantifies pairwise similarity between variables based on their association with the outcome. A supervised distance matrix is derived in two stages. The first stage involves a transformation based on a particular model for association. In particular, one might regress the outcome on each variable and then use the residuals or the influence curve from each regression as a data transformation. In the second stage, a choice of distance …

Analysis Of Subgroup Effects In Randomized Trials When Subgroup Membership Is Informatively Missing: Application To The Madit Ii Study, Daniel O. Scharfstein, Georgiana Onicescu, Steven Goodman

Johns Hopkins University, Dept. of Biostatistics Working Papers

In this paper, we develop and implement a general sensitivity analysis methodology for drawing inference about subgroup effects in a two-arm randomized trial when subgroup status is only known for a non-random sample in one of the trial arms. The methodology is developed in the context of the MADIT II study, a randomized trial designed to evaluate the effectiveness of implantable defibrillators on survival.

Causal Inference In Observational Studies With Outcome-Dependent Sampling, Weiwei Wang, Daniel Scharfstein, Zhiqiang Tan, Ellen J. Mackenzie

Johns Hopkins University, Dept. of Biostatistics Working Papers

In this paper, we consider estimation of the causal effect of a treatment on an outcome from observational data collected in two phases. In the first phase, a simple random sample of individuals are drawn from a population. On these individuals, information is obtained on treatment, outcome, and a few low-dimensional confounders. These individuals are then stratified according to these factors. In the second phase, a random sub-sample of individuals are drawn from each stratum, with known, stratum-specific selection probabilities. On these individuals, a rich set of confounding factors are collected. In this setting, we introduce four estimators: (1) simple …

Statistical Methods For Image Registration And Denoising, Matthew D. Sambora

Theses and Dissertations

This dissertation describes research into image processing techniques that enhance military operational and support activities. The research extends existing work on image registration by introducing a novel method that exploits local correlations to improve the performance of projection-based image registration algorithms. The dissertation also extends the bounds on image registration performance for both projection-based and full-frame image registration algorithms and extends the Barankin bound from the one-dimensional case to the problem of two-dimensional image registration. It is demonstrated that in some instances, the Cramer-Rao lower bound is an overly-optimistic predictor of image registration performance and that under some conditions, the …

Model-Based Clustering Of Methylation Array Data: A Recursive-Partitioning Algorithm For High-Dimensional Data Arising As A Mixture Of Beta Distributions, E. Andres Houseman, Brock C. Christensen, Ru-Fang Yeh, Carmen J. Marsit, Margaret R. Karagas, Margaret Wrensch, Heather H. Nelson, Joseph Wiemels, Shichun Zheng, John K. Wiencke, Karl T. Kelsey

Harvard University Biostatistics Working Paper Series

No abstract provided.

Confidence Intervals For The Population Mean Tailored To Small Sample Sizes, With Applications To Survey Sampling, Michael Rosenblum, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

The validity of standard confidence intervals constructed in survey sampling is based on the central limit theorem. For small sample sizes, the central limit theorem may give a poor approximation, resulting in confidence intervals that are misleading. We discuss this issue and propose methods for constructing confidence intervals for the population mean tailored to small sample sizes.

We present a simple approach for constructing confidence intervals for the population mean based on tail bounds for the sample mean that are correct for all sample sizes. Bernstein's inequality provides one such tail bound. The resulting confidence intervals have guaranteed coverage probability …

The Beginning Of The End For Chimpanzee Experiments?, Andrew Knight

Experimentation Collection

The advanced sensory, psychological and social abilities of chimpanzees confer upon them a profound ability to suffer when born into unnatural captive environments, or captured from the wild – as many older research chimpanzees once were – and when subsequently subjected to confinement, social disruption, and involuntary participation in potentially harmful biomedical research. Justifications for such research depend primarily on the important contributions advocates claim it has made toward medical advancements. However, a recent large-scale systematic review indicates that invasive chimpanzee experiments rarely provide benefits in excess of their profound animal welfare, bioethical and financial costs. The approval of large …

Simulation And Monte Carlo: With Applications In Finance And Mcmc, Samuel J. Frame

Statistics

No abstract provided.

The Detection Of Unsteady Flow Separation With Bioinspired Hair-Cell Sensors, Benjamin T. Dickinson, John R. Singler, Belinda A. Batten

Mathematics and Statistics Faculty Research & Creative Works

Biologists hypothesize that thousands of micro-scale hairs found on bat wings function as a network of air-flow sensors as part of a biological feedback flow control loop. In this work, we investigate hair-cell sensors as a means of detecting flow features in an unsteady separating flow over a cylinder. Individual hair-cell sensors were modeled using an Euler-Bernoulli beam equation forced by the fluid flow. When multiple sensor simulations are combined into an array of hair-cells, the response is shown to detect the onset and span of flow reversal, the upstream movement of the point of zero wall shear-stress, and the …

Approximate Low Rank Solutions Of Lyapunov Equations Via Proper Orthogonal Decomposition, John R. Singler

Mathematics and Statistics Faculty Research & Creative Works

We present an algorithm to approximate the solution Z of a stable Lyapunov equation AZ + ZA* + BB* = 0 using proper orthogonal decomposition (POD). This algorithm is applicable to large-scale problems and certain infinite dimensional problems as long as the rank of B is relatively small. In the infinite dimensional case, the algorithm does not require matrix approximations of the operators A and B. POD is used in a systematic way to provide convergence theory and simple a priori error bounds.

Stationary Statistical Properties Of Rayleigh-Bénard Convection At Large Prandtl Number, Xiaoming Wang

Mathematics and Statistics Faculty Research & Creative Works

This is the third in a series of our study of Rayleigh-Bénard convection at large Prandtl number. Here we investigate whether stationary statistical properties of the Boussinesq system for Rayleigh-Bénard convection at large Prandtl number are related to those of the infinite Prandtl number model for convection that is formally derived from the Boussinesq system via setting the Prandtl number to infinity. We study asymptotic behavior of stationary statistical solutions, or in-variant measures, to the Boussinesq system for Rayleigh-Bénard convection at large Prandtl number. in particular, we show that the invariant measures of the Boussinesq system for Rayleigh-Bénard convection converge …

Evaluating Statistical Methods Using Plasmode Data Sets In The Age Of Massive Public Databases: An Illustration Using False Discovery Rates, Gary L. Gadbury, Qinfang Xiang, Lin Yang, Stephen Barnes, Grier P. Page, David B. Allison

Mathematics and Statistics Faculty Research & Creative Works

Plasmode is a term coined several years ago to describe data sets that are derived from real data but for which some truth is known. Omic techniques, most especially microarray and genome wide association studies, have catalyzed a new zeitgeist of data sharing that is making data and data sets publicly available on an unprecedented scale. Coupling such data resources with a science of plasmode use would allow statistical methodologists to vet proposed techniques empirically (as opposed to only theoretically) and with data that are by definition realistic and representative. We illustrate the technique of empirical statistics by consideration of …

Effect Of Gutta Percha On The Retention Of A Post Using Different Cements, Saeda H. Basta

Loma Linda University Electronic Theses, Dissertations & Projects

This study evaluated the effect of residual gutta percha on the retention strength of prefabricated post cemented with four different cements. Forty intact mandibular single canal canines or premolars were selected for standardized size and quality, endodontically treated, and decoronated to the cementoenamel junction. A post space was prepared to accommodate Para post XP size 4 (Coltene, Whaledent Inc., Cuy Falls, Ohio) to a depth of 8 mm. The Obtura heated gutta percha system was used to inject a very thin line of gutta percha along one lateral wall to the depth of the future post. Forty obturated teeth were …

Knowledge, Perceptions, Beliefs And Behaviors Related To The Prevention Of Hypertension Among Black Seventh-Day Adventists Living In London, Maxine A. Newell

Loma Linda University Electronic Theses, Dissertations & Projects

This study was a cross-sectional survey of the hypertension (HTN) knowledge and risk behaviors of Black Seventh-day Adventists (SDA) in London. Recruitment took take place in 17 predominantly Black SDA churches in London. A questionnaire assessed knowledge and lay-beliefs about HTN and perceptions towards HTN using the health belief model (HBM) constructs of susceptibility, severity, benefits, barriers, and self-efficacy. Cohen’s Perceived Stress Scale was incorporated into the questionnaire. Blood pressure, height, weight and waist circumference were and current lifestyles practices were evaluated for the presence of HTN risk factors.

Of the 312 volunteers, ages 25 to 79, 55% were born …

Architecture And Implementation Of A Trust Model For Pervasive Applications, Sheikh Iqbal Ahamed, Mohammad Zulkernine, Sailaja Bulusu, Mehrab Monjur

Mathematics, Statistics and Computer Science Faculty Research and Publications

Collaborative effort to share resources is a significant feature of pervasive computing environments. To achieve secure service discovery and sharing, and to distinguish between malevolent and benevolent entities, trust models must be defined. It is critical to estimate a device's initial trust value because of the transient nature of pervasive smart space; however, most of the prior research work on trust models for pervasive applications used the notion of constant initial trust assignment. In this paper, we design and implement a trust model called DIRT. We categorize services in different security levels and depending on the service requester's context information, …

A Comparison Of Several Algorithms And Models For Analyzing Multivariate Normal Data With Missing Responses, Mojtaba Ganjali, H. Ranji

Applications and Applied Mathematics: An International Journal (AAM)

In this paper we compare some modern algorithms i.e. Direct Maximization of the Likelihood (DML), the EM algorithm, and Multiple Imputation (MI) for analyzing multivariate normal data with missing responses. We also compare two approaches for modeling incomplete data (1) ignoring missing data and (2) joint modeling of response and non-response mechanisms. Several types of Software which can be used to implement the above algorithms are also mentioned. We used these algorithms for a simulation study and to analyze a data set where outliers affect the parameter estimates and final conclusion. As the variance of the estimates cannot be obtained …

Some Applications Of Dirac's Delta Function In Statistics For More Than One Random Variable, Santanu Chakraborty

Applications and Applied Mathematics: An International Journal (AAM)

In this paper, we discuss some interesting applications of Dirac's delta function in Statistics. We have tried to extend some of the existing results to the more than one variable case. While doing that, we particularly concentrate on the bivariate case.

Comparison Of Microleakage In Three Sealant Placement Protocols Vs. The Enamel Loc System, Audrey T. Sheu

Loma Linda University Electronic Theses, Dissertations & Projects

PURPOSE: To evaluate sealant microleakage using four different application techniques.

METHODS: The four application techniques evaluated were acid etch only, Adper L-Pop bonding, Optibond bonding, and Enamel Loc One-Step Sealant. Sixty-four extracted third molar teeth were assigned on one of four application techniques (n=16) and Clinpro sealants applied according to the manufacturer's instructions. The teeth were thermocycled for 500 cycles, dyed, and embedded in resin. The samples were then sectioned and evaluated for microleakage on the buccal and lingual surfaces. Statistical methods employed to analyze the data were the Kruskal-Willis rank test and the Mann-Whitney U test.

RESULTS: No significant …

Doubly Robust Ecological Inference, Daniel B. Rubin, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

The ecological inference problem is a famous longstanding puzzle that arises in many disciplines. The usual formulation in epidemiology is that we would like to quantify an exposure-disease association by obtaining disease rates among the exposed and unexposed, but only have access to exposure rates and disease rates for several regions. The problem is generally intractable, but can be attacked under the assumptions of King's (1997) extended technique if we can correctly specify a model for a certain conditional distribution. We introduce a procedure that it is a valid approach if either this original model is correct or if we …

An Improved String Composition Method For Sequence Comparison, Guoqing Lu, Shunpu Zhang, Xiang Fang

Department of Statistics: Faculty Publications

Background: Historically, two categories of computational algorithms (alignment-based and alignment-free) have been applied to sequence comparison–one of the most fundamental issues in bioinformatics. Multiple sequence alignment, although dominantly used by biologists, possesses both fundamental as well as computational limitations. Consequently, alignment-free methods have been explored as important alternatives in estimating sequence similarity. Of the alignment-free methods, the string composition vector (CV) methods, which use the frequencies of nucleotide or amino acid strings to represent sequence information, show promising results in genome sequence comparison of prokaryotes. The existing CV-based methods, however, suffer certain statistical problems, thereby underestimating the amount of evolutionary …

An Improved String Composition Method For Sequence Comparison, Guoquing Lu, Shunpu Zhang, Xiang Fang

Biology Faculty Publications

Background: Historically, two categories of computational algorithms (alignment-based and alignment-free) have been applied to sequence comparison–one of the most fundamental issues in bioinformatics. Multiple sequence alignment, although dominantly used by biologists, possesses both fundamental as well as computational limitations. Consequently, alignment-free methods have been explored as important alternatives in estimating sequence similarity. Of the alignment-free methods, the string composition vector (CV) methods, which use the frequencies of nucleotide or amino acid strings to represent sequence information, show promising results in genome sequence comparison of prokaryotes. The existing CV-based methods, however, suffer certain statistical problems, thereby underestimating the amount of evolutionary …

Estimation Based On Case-Control Designs With Known Incidence Probability, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Case-control sampling is an extremely common design used to generate data to estimate effects of exposures or treatments on a binary outcome of interest when the proportion of cases (i.e., binary outcome equal to 1) in the population of interest is low. Case-control sampling represents a biased sample of a target population of interest by sampling a disproportional number of cases. Case-control studies are also commonly employed to estimate the effects of genetic markers or biomarkers on phenotypes. The typical approach used in practice is to fit (conditional) logistic regression models, ignoring the case-control sampling, in order to estimate the …

A Guide To Causal Parameters In Case-Control Designs: Targeted Maximum Likelihood Estimation, Sherri Rose, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Researchers of uncommon diseases are often interested in assessing potential risk factors. Given the low incidence of disease, these studies are frequently case-control in design, as this allows for a sufficient number of cases to be obtained without extensive sampling and can increase efficiency. However, these case-control samples are then biased since the proportion of cases in the sample is not the same as the population of interest. Methods for analyzing case-control studies have focused on utilizing logistic regression models that provide conditional and not causal estimates of the odds ratio. This article will demonstrate the use of the prevalence …

Optimal Interest Rate For A Borrower With Estimated Default And Prepayment Risk, Scott T. Howard

Theses and Dissertations

Today's mortgage industry is constantly changing, with adjustable rate mortgages (ARM), loans originated to the so-called "subprime" market, and volatile interest rates. Amid the changes and controversy, lenders continue to originate loans because the interest paid over the loan lifetime is profitable. Measuring the profitability of those loans, along with return on investment to the lender is assessed using Actuarial Present Value (APV), which incorporates the uncertainty that exists in the mortgage industry today, with many loans defaulting and prepaying. The hazard function, or instantaneous failure rate, is used as a measure of probability of failure to make a payment. …

Semiparametric Methods For Evaluating The Covariate-Specific Predictiveness Of Continuous Markers In Matched Case-Control Studies, Ying Huang, Margaret S. Pepe

UW Biostatistics Working Paper Series

To assess the value of a continuous marker in predicting the risk of a disease, a graphical tool called the predictiveness curve has been proposed. It characterizes the marker's predictiveness, or capacity to risk stratify the population by displaying the population distribution of risk endowed by the marker. Methods for making inference about the curve and for comparing curves in a general population have been developed. However, knowledge about a marker's performance in the general population only is not enough. Since a marker's effect on the risk model and its distribution can both differ across subpopulations, its predictiveness may vary …

The Impact Of Being Born With Cleft And Cleft Reparative Surgery On Overall Health And Speech Outcomes, Khatansuudal Evsanaa

Master's Theses

Orofacial cleft is one of the most common and treatable birth defects in the world. If left untreated, orofacial cleft can impair normal speech development, growth, and could lead to a number of health consequences later in life. The main motivation of the study is to measure the impact of being born with cleft and the cleft reparative surgery on overall speech and health cleft for teenagers in India using difference-in-differences approach along with household fixed effects method. An overall health outcome was measured using height, weight, grip strength and BMI, and the speech acceptability was measured using a “Universal …

The Weak Euler Scheme For Stochastic Delay Equations, Evelyn Buckwar, Rachel Kuske, Salah-Eldin A. Mohammed, Tony Shardlow

Articles and Preprints

We study weak convergence of an Euler scheme for non-linear stochastic delay differential equations (SDDEs) driven by multidimensional Brownian motion. The Euler scheme has weak order of convergence 1, as in the case of stochastic ordinary differential equations (SODEs) (i.e., without delay). The result holds for SDDEs with multiple finite fixed delays in the drift and diffusion terms. Although the set-up is non-anticipating, our approach uses the Malliavin calculus and the anticipating stochastic analysis techniques of Nualart and Pardoux.