Digital Commons Network^™

Grouped Data, Gudmund R. Iversen

Mathematics & Statistics Faculty Works

No abstract provided.

Multivariate Analysis Of Variance And Covariance (Manova And Mancova), Gudmund R. Iversen

Mathematics & Statistics Faculty Works

No abstract provided.

Random Error, Gudmund R. Iversen

Mathematics & Statistics Faculty Works

No abstract provided.

An Overview Of Reconstructability Analysis, Martin Zwick

Complex Systems Faculty Publications and Presentations

This paper is an overview of reconstructability analysis (RA), a discrete multivariate modeling methodology developed in the systems literature; an earlier version of this tutorial is Zwick (2001). RA was derived from Ashby (1964), and was developed by Broekstra, Cavallo, Cellier Conant, Jones, Klir, Krippendorff, and others (Klir, 1986, 1996). RA resembles and partially overlaps log‐line (LL) statistical methods used in the social sciences (Bishop et al., 1978; Knoke and Burke, 1980). RA also resembles and overlaps methods used in logic design and machine learning (LDL) in electrical and computer engineering (e.g. Perkowski et al., 1997). Applications of RA, like …

Uniqueness Theorems In Bioluminescence Tomography, Ge Wang, Yi Li, Ming Jiang

Mathematics and Statistics Faculty Publications

Motivated by bioluminescent imaging needs for studies on gene therapy and other applications in the mouse models, a bioluminescence tomography (BLT) system is being developed in the University of Iowa. While the forward imaging model is described by the well-known diffusion equation, the inverse problem is to recover an internal bioluminescent source distribution subject to Cauchy data. Our primary goal in this paper is to establish the solution uniqueness for BLT under practical constraints despite the ill-posedness of the inverse problem in the general case. After a review on the inverse source literature, we demonstrate that in the general case …

The Dual Spectral Set Conjecture, Steen Pedersen

Mathematics and Statistics Faculty Publications

Suppose that Λ = (aZ + b) ∪ (cZ + d) where a, b, c, d are real numbers such that a ≠ 0 and c ≠ 0. The union is not assumed to be disjoint. It is shown that the translates Ω + λ, λ is an element of Λ, tile the real line for some bounded measurable set Ω if and only if the exponentials e_λ(x) = e^i2πλx, λ is an element of Λ, form an orthogonal basis for some bounded measurable set Ω'.

A Comparison Of Modified Reconstructability Analysis And Ashenhurst‐Curtis Decomposition Of Boolean Functions, Anas Al-Rabadi, Marek Perkowski, Martin Zwick

Complex Systems Faculty Publications and Presentations

Modified reconstructability analysis (MRA), a novel decomposition technique within the framework of set‐theoretic (crisp possibilistic) reconstructability analysis, is applied to three‐variable NPN‐classified Boolean functions. MRA is superior to conventional reconstructability analysis, i.e. it decomposes more NPN functions. MRA is compared to Ashenhurst‐Curtis (AC) decomposition using two different complexity measures: log‐functionality, a measure suitable for machine learning, and the count of the total number of two‐input gates, a measure suitable for circuit design. MRA is superior to AC using the first of these measures, and is comparable to, but different from AC, using the second.

Arbitration And Litigation Of Employment Claims: An Empirical Comparison, Theodore Eisenberg, Elizabeth Hill

Cornell Law Faculty Publications

The authors conducted empirical research comparing court case and arbitrated outcomes for employment disputes. In cases not involving civil rights claims, they found little evidence that arbitrated outcomes materially differed from trial outcomes where the claimant was a higher-paid employee. Moreover, they found no statistically significant differences between employee win rates or in the median or mean awards in arbitration and litigation. They also reported evidence indicating that arbitrated disputes conclude more quickly than litigated disputes.

Forecasting The Equity Premium In The Australian Market, David E. Allen, Lurion Demello

Research outputs pre 2011

This paper provides a forecasting methodology for estimating the market risk premium in Australia. We employ an in-sample and out-of-sample forecast estimate using various dividend yield measures. The lagged dividend yield model is used to predict future equity premia on a data series that includes the top 85 percent of the Australian stock market. An important concern in this paper is the accuracy of dividend yield in forecasting the equity premium in the Australian market. We find that the level of predictability in the later part of the series is very weak compared to in-sample prediction during the 70s and …

Deviance Information Criterion For Comparing Stochastic Volatility Models, Andreas Berg, Renate Meyer, Jun Yu

Research Collection School Of Economics

Bayesian methods have been efficient in estimating parameters of stochastic volatility models for analyzing financial time series. Recent advances made it possible to fit stochastic volatility models of increasing complexity, including covariates, leverage effects, jump components, and heavy-tailed distributions. However, a formal model comparison via Bayes factors remains difficult. The main objective of this article is to demonstrate that model selection is more easily performed using the deviance information criterion (DIC). It combines a Bayesian measure of fit with a measure of model complexity. We illustrate the performance of DIC in discriminating between various different stochastic volatility models using simulated …

Wishartness And Independence Of Matrix Quadratic Forms For Kronecker Product Covariance Structures, Akhil K. Vaish, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

Let X be distributed as matrix normal with mean M and covariance matrix W⊗V, where W and V are nonnegative definite (nnd) matrices. In this paper we present a simple version of the Cochran’s theorem for matrix quadratic forms in X. The theorem is used to characterize the class of nnd matrices W such that the matrix quadratic forms that occur in multivariate analysis of variance are independent and Wishart except for a scale factor. © 2003 Elsevier Inc. All rights reserved.

Robust Likelihood-Based Analysis Of Multivariate Data With Missing Values, Rod Little, An Hyonggin

The University of Michigan Department of Biostatistics Working Paper Series

The model-based approach to inference from multivariate data with missing values is reviewed. Regression prediction is most useful when the covariates are predictive of the missing values and the probability of being missing, and in these circumstances predictions are particularly sensitive to model misspecification. The use of penalized splines of the propensity score is proposed to yield robust model-based inference under the missing at random (MAR) assumption, assuming monotone missing data. Simulation comparisons with other methods suggest that the method works well in a wide range of populations, with little loss of efficiency relative to parametric models when the latter …

Uncertainty And The Value Of Diagnostic Information With Application To Axillary Lymph Node Dissection In Breast Cancer, Giovanni Parmigiani

Johns Hopkins University, Dept. of Biostatistics Working Papers

In clinical decision making, it is common to ask whether, and how much, a diagnostic procedure is contributing to subsequent treatment decisions. Statistically, quantification of the value of the information provided by a diagnostic procedure can be carried out using decision trees with multiple decision points, representing both the diagnostic test and the subsequent treatments that may depend on the test's results. This article investigates probabilistic sensitivity analysis approaches for exploring and communicating parameter uncertainty in such decision trees. Complexities arise because uncertainty about a model's inputs determines uncertainty about optimal decisions at all decision nodes of a tree. We …

Marginalized Transition Models For Longitudinal Binary Data With Ignorable And Nonignorable Dropout, Brenda F. Kurland, Patrick J. Heagerty

UW Biostatistics Working Paper Series

We extend the marginalized transition model of Heagerty (2002) to accommodate nonignorable monotone dropout. Using a selection model, weakly identified dropout parameters are held constant and their effects evaluated through sensitivity analysis. For data missing at random (MAR), efficiency of inverse probability of censoring weighted generalized estimating equations (IPCW-GEE) is as low as 40% compared to a likelihood-based marginalized transition model (MTM) with comparable modeling burden. MTM and IPCW-GEE regression parameters both display misspecification bias for MAR and nonignorable missing data, and both reduce bias noticeably by improving model fit

Marginal Modeling Of Multilevel Binary Data With Time-Varying Covariates, Diana Miglioretti, Patrick Heagerty

UW Biostatistics Working Paper Series

We propose and compare two approaches for regression analysis of multilevel binary data when clusters are not necessarily nested: a GEE method that relies on a working independence assumption coupled with a three-step method for obtaining empirical standard errors; and a likelihood-based method implemented using Bayesian computational techniques. Implications of time-varying endogenous covariates are addressed. The methods are illustrated using data from the Breast Cancer Surveillance Consortium to estimate mammography accuracy from a repeatedly screened population.

Survival Model Predictive Accuracy And Roc Curves, Patrick Heagerty, Yingye Zheng

UW Biostatistics Working Paper Series

The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R^2, commonly used for continuous response models, or using extensions of sensitivity and specificity which are commonly used for binary response models.

In this manuscript we propose new time-dependent accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of …

Partly Conditional Survival Models For Longitudinal Data, Yingye Zheng, Patrick Heagerty

UW Biostatistics Working Paper Series

It is common in longitudinal studies to collect information on the time until a key clinical event, such as death, and to measure markers of patient health at multiple follow-up times. One approach to the joint analysis of survival and repeated measures data adopts a time-varying covariate regression model for the event time hazard. Using this standard approach the instantaneous risk of death at time t is specified as a possibly semi-parametric function of covariate information that has accrued through time t. In this manuscript we decouple the time scale for modeling the hazard from the time scale for accrual …

Semiparametric Estimation Of Time-Dependent: Roc Curves For Longitudinal Marker Data, Yingye Zheng, Patrick Heagerty

UW Biostatistics Working Paper Series

One approach to evaluating the strength of association between a longitudinal marker process and a key clinical event time is through predictive regression methods such as a time-dependent covariate hazard model. For example, a time-varying covariate Cox model specifies the instantaneous risk of the event as a function of the time-varying marker and additional covariates. In this manuscript we explore a second complementary approach which characterizes the distribution of the marker as a function of both the measurement time and the ultimate event time. Our goal is to flexibly extend the standard diagnostic accuracy concepts of sensitivity and specificity to …

Comparison Of The Inverse Probability Of Treatment Weighted (Iptw) Estimator With A Naïve Estimator In The Analysis Of Longitudinal Data With Time-Dependent Confounding: A Simulation Study, Thaddeus Haight, Romain Neugebauer, Ira B. Tager, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

A simulation study was conducted to compare estimates from a naïve estimator, using standard conditional regression, and an IPTW (Inverse Probability of Treatment Weighted) estimator, to true causal parameters for a given MSM (Marginal Structural Model). The study was extracted from a larger epidemiological study (Longitudinal Study of Effects of Physical Activity and Body Composition on Functional Limitation in the Elderly, by Tager et. al [accepted, Epidemiology, September 2003]), which examined the causal effects of physical activity and body composition on functional limitation. The simulation emulated the larger study in terms of the exposure and outcome variables of interest-- physical …

Multiple Testing. Part Ii. Step-Down Procedures For Control Of The Family-Wise Error Rate, Mark J. Van Der Laan, Sandrine Dudoit, Katherine S. Pollard

U.C. Berkeley Division of Biostatistics Working Paper Series

The present article proposes two step-down multiple testing procedures for asymptotic control of the family-wise error rate (FWER): the first procedure is based on maxima of test statistics (step-down maxT), while the second relies on minima of unadjusted p-values (step-down minP). A key feature of our approach is the test statistics null distribution (rather than data generating null distribution) used to derive cut-offs (i.e., rejection regions) for these test statistics and the resulting adjusted p-values. For general null hypotheses, corresponding to submodels for the data generating distribution, we identify an asymptotic domination condition for a null distribution under which the …

Multiple Testing. Part I. Single-Step Procedures For Control Of General Type I Error Rates, Sandrine Dudoit, Mark J. Van Der Laan, Katherine S. Pollard

U.C. Berkeley Division of Biostatistics Working Paper Series

The present article proposes general single-step multiple testing procedures for controlling Type I error rates defined as arbitrary parameters of the distribution of the number of Type I errors, such as the generalized family-wise error rate. A key feature of our approach is the test statistics null distribution (rather than data generating null distribution) used to derive cut-offs (i.e., rejection regions) for these test statistics and the resulting adjusted p-values. For general null hypotheses, corresponding to submodels for the data generating distribution, we identify an asymptotic domination condition for a null distribution under which single-step common-quantile and common-cut-off procedures asymptotically …

Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. Van Der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng

U.C. Berkeley Division of Biostatistics Working Paper Series

Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. Addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators, (ii) an approach for selecting an optimal estimator among these candidates, and (iii) a method for reliably assessing the performance of the resulting estimator. We propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. In this approach, the parameter of interest is defined as the risk minimizer for a suitable …

Kernel Estimation Of Rate Function For Recurrent Event Data, Chin-Tsang Chiang, Mei-Cheng Wang, Chiung-Yu Huang

Johns Hopkins University, Dept. of Biostatistics Working Papers

Recurrent event data are largely characterized by the rate function but smoothing techniques for estimating the rate function have never been rigorously developed or studied in statistical literature. This paper considers the moment and least squares methods for estimating the rate function from recurrent event data. With an independent censoring assumption on the recurrent event process, we study statistical properties of the proposed estimators and propose bootstrap procedures for the bandwidth selection and for the approximation of confidence intervals in the estimation of the occurrence rate function. It is identified that the moment method without resmoothing via a smaller bandwidth …

Optimization Of Breast Cancer Screening Modalities, Yu Shen, Giovanni Parmigiani

Johns Hopkins University, Dept. of Biostatistics Working Papers

Mathematical models and decision analyses based on microsimulations have been shown to be useful in evaluating relative merits of various screening strategies in terms of cost and mortality reduction. Most investigations regarding the balance between mortality reduction and costs have focused on a single modality, mammography. A systematic evaluation of the relative expenses and projected benefit of combining clinical breast examination and mammography is not at present available. The purpose of this report is to provide methodologic details including assumptions and data used in the process of modeling for complex decision analyses, when searching for optimal breast cancer screening strategies …

Modeling The Incubation Period Of Anthrax, Ron Brookmeyer, Elizabeth Johnson, Sarah Barry

Johns Hopkins University, Dept. of Biostatistics Working Papers

Models of the incubation period of anthrax are important to public health planners because they can be used to predict the delay before outbreaks are detected, the size of an outbreak and the duration of time that persons should remain on antibiotics to prevent disease. The difficulty is that there is little direct data about the incubation period in humans. The objective of this paper is to develop and apply models for the incubation period of anthrax. Mechanistic models that account for the biology of spore clearance and germination are developed based on a competing risks formulation. The models predict …

A Robust Two-Sample Procedure To Estimate A Shift Parameter, Feridun Tasdan

Dissertations

This study estimates the location shift parameter in the two-sample problem. The classical method, Least Square(LS), obtains the shift parameter estimate under the normality assumption. A departure from normality assumption makes the estimate inefficient and unreliable. One alternative to the least square estimate is Hodges-Lehmann (HL) estimate which uses Wilcoxon ranks to estimate the shift parameter. This estimate is robust against contaminations and large outliers. The proposed method in this study combines two samples and uses convolution technique to find a density function for the combined sample. This new density function is later used in the construction of the log …

Global Solutions To The Lake Equations With Isolated Vortex Regions, Chaocheng Huang

Mathematics and Statistics Faculty Publications

The vorticity formulation for the lake equations in R² is studied.

Determination Of Spatial Strata For Environmental Regulatory Purposes, John Edward Daniels

Dissertations

This dissertation introduces spatial strata modelling, a methodology that combines spatial statistics, cluster analysis, and geographic information system theories to analyze the background level of naturally occurring contaminants of concern (COCs). The objective of spatial strata modelling is to divide a geographic area of interest into mutually exclusive geographic zones (spatial strata): with each stratum representing a different level of COC concentration. An estimate of each stratum's COC concentration level, representing an upper regulatory limit, will also be provided. Data provided by the Michigan Department of Environmental Quality describing the spatial location and arsenic concentrations of 211 Michigan sites (arsenic …

Clinical Effectiveness Of A Subperiosteal Anchorage Device, Monica Anne Witte

Loma Linda University Electronic Theses, Dissertations & Projects

The purpose of this pilot study was to evaluate the clinical efficacy of a subperiosteal anchorage device, the palatal OnPlant™, during orthodontic retraction of protruding anterior teeth in cases requiring maxillary premolar extraction. Seven subjects (5 female, 2 male), ages 13 to 55, were selected for the study. The OnPlant was surgically placed in the mid-palatal region through a well-defined subperiosteal tunnel. Following the manufacturer recommended osseointegration period of four months, the OnPlant was uncovered and attached to the first molars by means of a transpalatal bar. Standard orthodontic treatment then commenced to retract the anterior teeth after the first …

Comparison Of Bracket Bond Strength By Total, Self-Etch And Laser Treatment, Kyo Sung Shawn Kim

Loma Linda University Electronic Theses, Dissertations & Projects

Laser etching of enamel surfaces alters the physical and chemical characteristics of the enamel. These changes in characteristics enhance the bonding to enamel. The purpose of this study was to compare the shear and tensile bond strength of Er,Cr:YSGG hydrokinetic laser system (Biolase) with 37% phosphoric acid and self etching primer 30 minutes and 72 hours after bonding. Four different laser power output setting was observed: 1.5W, 2.0W, 2.5W, and 3.0W. Two hundred forty bovine teeth free of defect, caries, and dentin exposure were mounted in acrylic resin and divided into 24 groups of 10 teeth. Sixteen groups of 10 …