Browsing Statistics by Title
Now showing items 78-97 of 108
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R-squared inference under non-normal error
Assessment of the relationship between diet and health status, especially association between diet and chronic disease risk, has attracted lot of research interest in statistical and epidemiologic studies. However, due to ... -
R-squared inference under non-normal error
Assessment of the relationship between diet and health status, especially association between diet and chronic disease risk, has attracted lot of research interest in statistical and epidemiologic studies. However, due to ... -
Realized genome sharing in random effects models for quantitative genetic traits
DNA copies inherited from the same ancestral copy by related individuals are said to be identical by descent (IBD). IBD gives rise to genetic similarities between related individuals. In quantitative genetics, two fundamental ... -
Representation Learning for Partitioning Problems
This dissertation addresses representation learning for partitioning problems. Clustering a set of data points and segmenting a time series of data points are two classical partitioning problems. Nonparametric methods such ... -
A resampling approach to clustering with confidence
(2012-09-13)We propose a method for estimating the number of groups in a data set. Our method is an extension of Generalized Single Linkage clustering (GSL) (Stuetzle and Nugent 2010), a nonparametric clustering method based on the ... -
Robust estimation of factor models in finance
(2005)Standard asset-pricing models entail expressions for expected returns in terms of coefficients relative to risk factors. Methods to estimate premiums of risk factors have, at its core, a single or multiple linear regression ... -
Scalable Learning in Latent State Sequence Models
In this dissertation, we develop scalable learning methods for sequential data models with latent (hidden) states. State space models (SSMs) and recurrent neural networks (RNNs) are popular models for sequential data using ... -
Scalable Manifold Learning and Related Topics
The subject of manifold learning is vast and still largely unexplored. As a subset of unsupervised learning it has a fundamental challenge in adequately defining the problem but whose solution is to an increasingly important ... -
Scalable Methods for the Inference of Identity by Descent
Identity by descent (IBD) describes the shared inheritance of DNA and underlies genetic similarity between individuals. Estimated IBD graphs describing the IBD relationships among individuals have many uses in statistical ... -
Shape-Constrained Inference for Concave-Transformed Densities and their Modes
(2013-11-14)We consider inference about functions estimated via shape constraints based on concavity. We consider log-concave densities and other “concave-transformed” densities on the real line, where a concave-transformed class is ... -
Space-Time Contour Models for Sea Ice Forecasting
This dissertation develops statistical methods for modeling contours. Particular emphasis is placed on forecasting the sea ice edge contour, or the boundary around ocean areas that are ice-covered. Current sea ice forecasts ... -
Space-Time Smoothing Models for Surveillance and Complex Survey Data
Area and time-specific estimates of disease rates, cause-specific mortality rates and other key health indicators are of great interest for health care and policy purposes. Such estimates provide the information needed to ... -
Statistical analysis of low-frequency earthquake catalogs
Low-frequency earthquakes (LFEs) are small magnitude (less than 2) earthquakes, with reduced amplitudes at frequencies greater than 10 Hz relative to ordinary small earthquakes. They are usually grouped into families of ... -
Statistical Divergences for Learning and Inference: Limit Laws and Non-Asymptotic Bounds
Statistical divergences have been widely used in statistics and artificial intelligence to measure the dissimilarity between probability distributions. The applications range from generative modeling to statistical inference. ... -
Statistical Hurdle Models for Single Cell Gene Expression: Differential Expression and Graphical Modeling
This dissertation describes a set of statistical methods developed for analysis of single cell gene expression. A characteristic of single cell expression is bimodal expression, in which two clusters of expression are ... -
Statistical inference using Kronecker structured covariance
(2013-11-14)We present results for testing and estimation in the context of separable covariance models. We concentrate on two types of data: relational data and cross-classified data. Relational data is frequently represented by a ... -
Statistical Methods for Adaptive Immune Receptor Repertoire Analysis and Comparison
B and T cell receptors, also known as adaptive immune receptors, perform key roles in adaptive immunity. These proteins identify and deal with foreign invaders like viruses or bacteria, allowing for robust and long-lasting ... -
Statistical Methods for Clustering and High Dimensional Time Series Analysis
This dissertation mainly explores two statistical tasks, namely clustering and analysis of high-dimensional time series. Clustering, a very important unsupervised learning problem, studies the structure of unlabeled datasets. ... -
Statistical methods for genomic sequencing data
Genomic sequencing data has revolutionized our understanding of the genetic basis of biological processes. The cost of sequencing the first human genome was estimated to be greater than 50 million dollars. However, with ... -
Statistical Methods for Geospatial Modeling with Stratified Cluster Survey Data
The production of fine-scale, pixel level maps have become increasingly common in the current era of precision public health. This has led to the use of cluster level spatial models by major organizations such as WorldPop ...