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Some of the material in is restricted to members of the community. By logging in, you may be able to gain additional access to certain collections or items. If you have questions about access or logging in, please use the form on the Contact Page.
Clustering is an essential data mining tool that aims to discover inherent cluster structure in data. As such, the study of clusterability, which evaluates whether data possesses such structure, is an integral part of cluster analysis....
Statisticians often encounter data in the form of a combination of discrete and continuous outcomes. A special case is zero-inflated longitudinal data where the response variable has a large portion of zeros. These data exhibit...
Analytical models developed using field data can provide useful information with acceptable confidence to evaluate and predict the operational characteristics of a highway. As such, this study presents statistical models that can be used...
On nonparametric regression for current status data
Description:
In some studies, it is not possible to observe directly the time at which an event of interest occurs, instead each experimental unit is examined at one time only, and it is noted whether or not the event of interest has occurred. This... We show that the estimate of the conditional distribution function from the LNPML method can be characterized as a solution to an isotonic regression problem and hence easily computed. This estimate does well in our simulation studies....
Evaluating the performance of models predicting a binary outcome can be done using a variety of measures. While some measures intend to describe the model's overall fit, others more accurately describe the model's ability to discriminate...
The past several decades have seen great advances in the field of organizational politics. At the individual level, political skill has garnered the majority of the scholarly focus, whereas it's motivational counterpart, political will, ...
TESTING WHETHER NEW IS BETTER THAN USED OF A SPECIFIED AGE
Description:
This research contributes to the theory and methods of testing hypotheses for classes of life distributions. Two classes of life distributions considered in this dissertation are: (1) The New Better Than Used (NBU) Class: The life... The NBU and NBU-t(, 0) classes have dual classes (New Worse Than Used and New Worse Than Used At t(, 0), respectively) defined by reversing the inequality. The NBU-t(, 0) class is a new class of life distributions and contains the NBU class. We study the basic properties of the NBU-t(, 0) class and propose a test of H(, 0): F(x+t(, 0))(' )=(' )F(x)F(t(, 0)) for all x (GREATERTHEQ) 0, versus H(, A... We extend our test of H(, 0) versus H(, A) to accommodate randomly censored data. For the censored data situation our test is based on the statistic (DIAGRAM, TABLE OR GRAPHIC OMITTED...PLEASE SEE DAI) where F is the Kaplan-Meier (1958, J. Amer. Statist. Assoc. 53, 457-481) estimator of(' )F. Under mild regularity conditions on the amount of censoring, a consistent test of H(, A) for the randomly censored model is obtained. In Chapter III we develop a two-sample NBU test of the null hypothesis that two distributions F and G are equal, versus the alternative that F is "more NBU" than is G. Our test is based on the statistic (DIAGRAM, TABLE OR GRAPHIC OMITTED...PLEASE SEE DAI) where m and n are the sample sizes from F and G, and F(, m) and G(, n) are the empirical distributions of F and G. Asymptotic normality of T(, m, n), suitably normalized, is a direct consequence of Hoeffding's (1948, Ann. Math. Statist. 19, ... Our test of H(, A) utilizes the Kaplan-Meier estimator. However, there are other possible estimators of the survival function for the randomly censored model. . . . (Author's abstract exceeds stipulated maximum length....
In the classical literature of Statistics, a large amount of methods have been addressed for data analysis on Euclidean space. Over the past few decades, however, a growing interest has been devoted to non-Euclidean data analysis. In...
Polychotomous quantal response models are widely used in medical and econometric studies to analyze categorical or ordinal data. In this study, we apply the Bayesian methodology through a mixed-effects polychotomous quantal response...
Image analysis often requires dimension reduction before statistical analysis, in order to apply sophisticated procedures. Motivated by eventual applications, a variety of criteria have been proposed: reconstruction error, class...
Most of the data encountered is bounded nonlinear data. The Universe is bounded, planets are sphere like shaped objects, and life growing on Earth comes in various shapes and colors that can hardly be represented as points on a linear...
Two main challenges in computational biology are identify differential expressed genes from gene expression data and find out biological variable interactions from genomics data. This dissertation presents two studies in each of them. In...
Recurrent events data are rising in all areas of biomedical research. We present a model for recurrent events data with the same link for the intensity and mean functions. Simple interpretations of the covariate effects on both the...
With rapid advances in data acquisition and storage techniques, modern scientific investigations in epidemiology, genomics, imaging and networks are increasingly producing challenging data structures in the form of high-dimensional...
Statistical depth, a commonly used analytic tool in non-parametric statistics, has been extensively studied for multivariate and functional observations over the past few decades. Although various forms of depth were introduced, they are...
Motivated by understanding the devastating financial crisis in 2008 that was partially caused by underestimation of financial risk, we propose a class of time-varying mixture models for risk analysis and management. There are various...
Multivariate response models are being used increasingly more in almost all fields with the necessary employment of inferential methods such as Canonical Correlation Analysis (CCA). This requires the estimation of the number of...
We develop a modeling framework to simultaneously evaluate various types of predictability in stock returns, including stocks' sensitivity ("betas") to systematic risk factors, stocks' abnormal returns unexplained by risk factors (...
In research synthesis, researchers may aim at summarizing peoples' attitudes and perceptions of phenomena that have been assessed using different measures. Self-report rating scales are among the most commonly used measurement tools to...
Testing for a time-dependent covariate effect in the linear risk model
Description:
We propose two tests to identify a time dependent covariate effect in the partly parametric linear risk model, and derive asymptotic distributions of the test statistics under the assumption that the covariate effect of interest is...
Our view is that while some of the basic principles of data analysis are going to remain unchanged, others are to be gradually replaced with Geometry and Topology methods. Linear methods are still making sense for functional data...
STOCHASTIC VERSIONS OF REARRANGEMENT INEQUALITIES WITH APPLICATIONS TO STATISTICS
Description:
In this dissertation we develop a theory which offers a unified approach to the problem of obtaining stochastic versions of deterministic rearrangement inequalities. To develop the theory we first define two new classes of functions and establish preservation properties of these functions under various statistical and mathematical operations. Next we introduce the notion of stochastically similarly arranged (SSA) pairs of random vectors. We prove that if the random vectors (X, Y) are SSA and the function f from R('n) x R('n) into R('n) is monotone with respect to a certain... (DIAGRAM, TABLE OR GRAPHIC OMITTED...PLEASE SEE DAI) hold. This result yields a unified way of obtaining stochastic versions of rearrangement inequalities. We then show that many multivariate densities of interest in statistical practice govern pairs of random vectors which are SSA. Next we show that under certain statistical operations on pairs of SSA random vectors the property of being SSA is preserved. For example, we show that the rank order of SSA random variables is SSA. We also show that the SSA property is... Finally, we show how the results we obtain can be applied to problems in hypothesis testing.
Coefficient Omega was introduced by McDonald (1978) as a reliability coefficient of composite scores for the congeneric model. Interval estimation (Neyman, 1937) on coefficient Omega provides a range of plausible values which is likely...
Convolutional Neural Networks (CNNs) are widely used and have an impressive performance in detecting and classifying objects. However, the CNN's performance is sensitive to variations in rotation, position or scaling of the objects to be...
This thesis proposes a new variation propagation modeling and group EWMA control chart method for quality improvement in multistage process that aims to detect and isolate the largest variation propagation and faulty stages in a...
LUMPABILITY AND WEAK LUMPABILITY IN FINITE MARKOV CHAINS
Description:
Consider a Markov chain x(t), t = 0, 1, 2, ..., with a finite state space, N = {1, n}, transition probability matrix P = (p(, ij)) i, j (epsilon) N, and an initial probability vector V = (v(, i)) i (epsilon) N. For m (LESSTHEQ) n... (DIAGRAM, TABLE OR GRAPHIC OMITTED...PLEASE SEE DAI) The new process y(t), called a function of Markov chain, need not be Markov. If y(t) is again Markov, whatever the initial probability vector of x(t), x(t) is said to be lumped to y(t) with respect to the partition A. If y(t) is again... Conditions under which x(t) can be lumped or weakly lumped to y(t) with respect to A, are introduced. Relationships between the two processes x(t) and y(t) and the properties of the new process y(t) are discussed. Criteria are developed to determine whether a given Markov chain can be weakly lumped with respect to a given partition in terms of an analysis of systems of linear equations. Necessary and sufficient conditions on the transition... Lumpability and weak lumpability are also studied for Markov chains which are not irreducible. This involves a study of the interplay between two partitions of the state space N, the partition C, induced by the closed sets of states of... Suppose a process y(t) is known to arise as a result of a weak lumping or lumping from some unknown Markov chain x(t). Let (chi)(t) be the class of all Markov chains x(t) with n states which yield this weak lumping or lumping. The... The importance of lumpability in application is discussed.
This research work is an attempt to illustrate the versatility and wide applications of the field of statistical science. Specifically, the research work involves the application of statistics in the field of law. The application will...
With the increasing popularity of information technology, especially electronic imaging techniques, large amount of high dimensional data such as 3D shapes become pervasive in science, engineering and even people's daily life, in the...
The high mortality rate and huge expenditure caused by dementia makes it a pressing concern for public health researchers. Among the potential risk factors in diet and nutrition, the relation between alcohol usage and dementia has been...
The Barker Hypothesis states that maternal and `in utero' attributes during pregnancy affects a child's cardiovascular health throughout life. We present an analysis of a unique longitudinal dataset from Jamaica that consists of three...
Regression models for spatial binary data with application to the distribution of plant species
Description:
We propose models for spatial binary data which incorporate covariate information, spatial interaction and spatial smoothness. We explore one of these models (Besag's autologistic model with covariates) in detail. We describe three...
Recent advances in computing and measurement technologies have led to an explosion in the amount of data that are being collected in many areas of application. Much of these data have network or graph structures, and they are common in...
In this study, we will examine the Bayesian Dynamic Survival Models, time-varying coefficients models from a Bayesian perspective, and their applications in the aging setting. The specific questions we are interested in are: Do the...
Longitudinal studies are widely used in various fields, such as public health, clinic trials and financial data analysis. A major challenge for longitudinal studies is repeated measurements from each subject, which cause time dependent...
Some of the material in is restricted to members of the community. By logging in, you may be able to gain additional access to certain collections or items. If you have questions about access or logging in, please use the form on the Contact Page.
