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Articles 1 - 30 of 65
Full-Text Articles in Multivariate Analysis
Multivariate Spectral Analysis Of Crism Data To Characterize The Composition Of Mawrth Vallis, Melissa Luna
Multivariate Spectral Analysis Of Crism Data To Characterize The Composition Of Mawrth Vallis, Melissa Luna
Melissa Luna
No abstract provided.
Implicit Copulas From Bayesian Regularized Regression Smoothers, Nadja Klein, Michael S. Smith
Implicit Copulas From Bayesian Regularized Regression Smoothers, Nadja Klein, Michael S. Smith
Michael Stanley Smith
Variational Bayes Estimation Of Discrete-Margined Copula Models With Application To Ime Series, Ruben Loaiza-Maya, Michael S. Smith
Variational Bayes Estimation Of Discrete-Margined Copula Models With Application To Ime Series, Ruben Loaiza-Maya, Michael S. Smith
Michael Stanley Smith
The Finney County, Kansas Community Assessment Process: Fact Book, Debra J. Bolton Phd, Shannon L. Dick M.S.
The Finney County, Kansas Community Assessment Process: Fact Book, Debra J. Bolton Phd, Shannon L. Dick M.S.
Dr. Debra Bolton
This multi-lingual/multi-cultural study was called, Community Assets Processt, by the groups that “commissioned” it: Finnup Foundation, Finney County K-State Research & Extension, Western Kansas Community Foundation, Finney County United Way, Finney County Health Department, United Methodist Community Health Center (UMMAM), Center for Children and Families, Garden City Recreation Commission, and the Garden City Cultural Relations Board, because we intend for this to be an ongoing discussion. An objective, for those promoting the study, was to connect foundation, state, and federal funding with activities or services that addressed the true needs of people living in Finney County. The group was looking …
Online Variational Bayes Inference For High-Dimensional Correlated Data, Sylvie T. Kabisa, Jeffrey S. Morris, David Dunson
Online Variational Bayes Inference For High-Dimensional Correlated Data, Sylvie T. Kabisa, Jeffrey S. Morris, David Dunson
Jeffrey S. Morris
High-dimensional data with hundreds of thousands of observations are becoming commonplace in many disciplines. The analysis of such data poses many computational challenges, especially when the observations are correlated over time and/or across space. In this paper we propose exible hierarchical regression models for analyzing such data that accommodate serial and/or spatial correlation. We address the computational challenges involved in fitting these models by adopting an approximate inference framework. We develop an online variational Bayes algorithm that works by incrementally reading the data into memory one portion at a time. The performance of the method is assessed through simulation studies. …
Shrinkage Estimation For Multivariate Hidden Markov Mixture Models, Mark Fiecas, Jürgen Franke, Rainer Von Sachs, Joseph Tadjuidje
Shrinkage Estimation For Multivariate Hidden Markov Mixture Models, Mark Fiecas, Jürgen Franke, Rainer Von Sachs, Joseph Tadjuidje
Mark Fiecas
Bayesian Function-On-Function Regression For Multi-Level Functional Data, Mark J. Meyer, Brent A. Coull, Francesco Versace, Paul Cinciripini, Jeffrey S. Morris
Bayesian Function-On-Function Regression For Multi-Level Functional Data, Mark J. Meyer, Brent A. Coull, Francesco Versace, Paul Cinciripini, Jeffrey S. Morris
Jeffrey S. Morris
Medical and public health research increasingly involves the collection of more and more complex and high dimensional data. In particular, functional data|where the unit of observation is a curve or set of curves that are finely sampled over a grid -- is frequently obtained. Moreover, researchers often sample multiple curves per person resulting in repeated functional measures. A common question is how to analyze the relationship between two functional variables. We propose a general function-on-function regression model for repeatedly sampled functional data, presenting a simple model as well as a more extensive mixed model framework, along with multiple functional posterior …
Functional Regression, Jeffrey S. Morris
Functional Regression, Jeffrey S. Morris
Jeffrey S. Morris
Functional data analysis (FDA) involves the analysis of data whose ideal units of observation are functions defined on some continuous domain, and the observed data consist of a sample of functions taken from some population, sampled on a discrete grid. Ramsay and Silverman's 1997 textbook sparked the development of this field, which has accelerated in the past 10 years to become one of the fastest growing areas of statistics, fueled by the growing number of applications yielding this type of data. One unique characteristic of FDA is the need to combine information both across and within functions, which Ramsay and …
Equivalence Of Kernel Machine Regression And Kernel Distance Covariance For Multidimensional Trait Association Studies, Wen-Yu Hua, Debashis Ghosh
Equivalence Of Kernel Machine Regression And Kernel Distance Covariance For Multidimensional Trait Association Studies, Wen-Yu Hua, Debashis Ghosh
Debashis Ghosh
Associating genetic markers with a multidimensional phenotype is an important yet challenging problem. In this work, we establish the equivalence between two popular methods: kernel-machine regression (KMR), and kernel distance covariance (KDC). KMR is a semiparametric regression framework that models covariate effects parametrically and genetic markers non-parametrically, while KDC represents a class of methods that include distance covariance (DC) and Hilbert-Schmidt independence criterion (HSIC), which are nonparametric tests of independence. We show that the equivalence between the score test of KMR and the KDC statistic under certain conditions can lead to a novel generalization of the KDC test that incorporates …
Depicting Estimates Using The Intercept In Meta-Regression Models: The Moving Constant Technique, Blair T. Johnson Dr., Tania B. Huedo-Medina Dr.
Depicting Estimates Using The Intercept In Meta-Regression Models: The Moving Constant Technique, Blair T. Johnson Dr., Tania B. Huedo-Medina Dr.
Blair T. Johnson
In any scientific discipline, the ability to portray research patterns graphically often aids greatly in interpreting a phenomenon. In part to depict phenomena, the statistics and capabilities of meta-analytic models have grown increasingly sophisticated. Accordingly, this article details how to move the constant in weighted meta-analysis regression models (viz. “meta-regression”) to illuminate the patterns in such models across a range of complexities. Although it is commonly ignored in practice, the constant (or intercept) in such models can be indispensible when it is not relegated to its usual static role. The moving constant technique makes possible estimates and confidence intervals at …
A Comparative Analysis Of Decision Trees Vis-À-Vis Other Computational Data Mining Techniques In Automotive Insurance Fraud Detection, Adrian Gepp, Kuldeep Kumar, J Holton Wilson, Sukanto Bhattacharya
A Comparative Analysis Of Decision Trees Vis-À-Vis Other Computational Data Mining Techniques In Automotive Insurance Fraud Detection, Adrian Gepp, Kuldeep Kumar, J Holton Wilson, Sukanto Bhattacharya
Kuldeep Kumar
No abstract provided.
Multiple Comparison Procedures For Neuroimaging Genomewide Association Studies, Wen-Yu Hua, Thomas E. Nichols, Debashis Ghosh
Multiple Comparison Procedures For Neuroimaging Genomewide Association Studies, Wen-Yu Hua, Thomas E. Nichols, Debashis Ghosh
Debashis Ghosh
Recent research in neuroimaging has been focusing on assessing associations between genetic variants measured on a genomewide scale and brain imaging phenotypes. Many publications in the area use massively univariate analyses on a genomewide basis for finding single nucleotide polymorphisms that influence brain structure. In this work, we propose using various dimensionalityreduction methods on both brain MRI scans and genomic data, motivated by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. We also consider a new multiple testing adjustments inspired from the idea of local false discovery rate of Efron and others (2001). Our proposed procedure is able to find associations …
Garma Toolbox For Matlab, Mehdi Jalalpour
From Amazon To Apple: Modeling Online Retail Sales, Purchase Incidence And Visit Behavior, Anastasios Panagiotelis, Michael S. Smith, Peter Danaher
From Amazon To Apple: Modeling Online Retail Sales, Purchase Incidence And Visit Behavior, Anastasios Panagiotelis, Michael S. Smith, Peter Danaher
Michael Stanley Smith
In this study we propose a multivariate stochastic model for website visit duration, page views, purchase incidence and the sale amount for online retailers. The model is constructed by composition from carefully selected distributions, and involves copula components. It allows for the strong nonlinear relationships between the sales and visit variables to be explored in detail, and can be used to construct sales predictions. The model is readily estimated using maximum likelihood, making it an attractive choice in practice given the large sample sizes that are commonplace in online retail studies. We examine a number of top-ranked U.S. online retailers, …
Spectral Density Shrinkage For High-Dimensional Time Series, Mark Fiecas, Rainer Von Sachs
Spectral Density Shrinkage For High-Dimensional Time Series, Mark Fiecas, Rainer Von Sachs
Mark Fiecas
Towards Real-Time, On-Board, Hardware-Supported Sensor And Software Health Management For Unmanned Aerial Systems, Johann Schumann, Kristin Y. Rozier, Thomas Reinbacher, Ole J. Mengshoel, Timmy Mbaya, Corey Ippolito
Towards Real-Time, On-Board, Hardware-Supported Sensor And Software Health Management For Unmanned Aerial Systems, Johann Schumann, Kristin Y. Rozier, Thomas Reinbacher, Ole J. Mengshoel, Timmy Mbaya, Corey Ippolito
Ole J Mengshoel
Optimizing Parallel Belief Propagation In Junction Trees Using Regression, Lu Zheng, Ole J. Mengshoel
Optimizing Parallel Belief Propagation In Junction Trees Using Regression, Lu Zheng, Ole J. Mengshoel
Ole J Mengshoel
Exploring Multiple Dimensions Of Parallelism In Junction Tree Message Passing, Lu Zheng, Ole J. Mengshoel
Exploring Multiple Dimensions Of Parallelism In Junction Tree Message Passing, Lu Zheng, Ole J. Mengshoel
Ole J Mengshoel
Latent Topic Analysis For Predicting Group Purchasing Behavior On The Social Web, Feng-Tso Sun, Martin Griss, Ole J. Mengshoel, Yi-Ting Yeh
Latent Topic Analysis For Predicting Group Purchasing Behavior On The Social Web, Feng-Tso Sun, Martin Griss, Ole J. Mengshoel, Yi-Ting Yeh
Ole J Mengshoel
Mobile Computing: Challenges And Opportunities For Autonomy And Feedback, Ole J. Mengshoel, Bob Iannucci, Abe Ishihara
Mobile Computing: Challenges And Opportunities For Autonomy And Feedback, Ole J. Mengshoel, Bob Iannucci, Abe Ishihara
Ole J Mengshoel
Software Health Management With Bayesian Networks, Johann Schumann, Timmy Mbaya, Ole J. Mengshoel, Knot Pipatsrisawat, Ashok Srivastava, Arthur Choi, Adnan Darwiche
Software Health Management With Bayesian Networks, Johann Schumann, Timmy Mbaya, Ole J. Mengshoel, Knot Pipatsrisawat, Ashok Srivastava, Arthur Choi, Adnan Darwiche
Ole J Mengshoel
Global Quantitative Assessment Of The Colorectal Polyp Burden In Familial Adenomatous Polyposis Using A Web-Based Tool, Patrick M. Lynch, Jeffrey S. Morris, William A. Ross, Miguel A. Rodriguez-Bigas, Juan Posadas, Rossa Khalaf, Diane M. Weber, Valerie O. Sepeda, Bernard Levin, Imad Shureiqi
Global Quantitative Assessment Of The Colorectal Polyp Burden In Familial Adenomatous Polyposis Using A Web-Based Tool, Patrick M. Lynch, Jeffrey S. Morris, William A. Ross, Miguel A. Rodriguez-Bigas, Juan Posadas, Rossa Khalaf, Diane M. Weber, Valerie O. Sepeda, Bernard Levin, Imad Shureiqi
Jeffrey S. Morris
Background: Accurate measures of the total polyp burden in familial adenomatous polyposis (FAP) are lacking. Current assessment tools include polyp quantitation in limited-field photographs and qualitative total colorectal polyp burden by video.
Objective: To develop global quantitative tools of the FAP colorectal adenoma burden.
Design: A single-arm, phase II trial.
Patients: Twenty-seven patients with FAP.
Intervention: Treatment with celecoxib for 6 months, with before-treatment and after-treatment videos posted to an intranet with an interactive site for scoring.
Main Outcome Measurements: Global adenoma counts and sizes (grouped into categories: less than 2 mm, 2-4 mm, and greater than 4 mm) were …
Sas Macro: Kappa Statistic For Clustered Matched-Pair Data, Zhao Yang
Sas Macro: Kappa Statistic For Clustered Matched-Pair Data, Zhao Yang
Zhao (Tony) Yang, Ph.D.
The SAS macro was developed to calculate the kappa statistic for the clustered matched-pair data.
A Comparison Of Periodic Autoregressive And Dynamic Factor Models In Intraday Energy Demand Forecasting, Thomas Mestekemper, Goeran Kauermann, Michael Smith
A Comparison Of Periodic Autoregressive And Dynamic Factor Models In Intraday Energy Demand Forecasting, Thomas Mestekemper, Goeran Kauermann, Michael Smith
Michael Stanley Smith
We suggest a new approach for forecasting energy demand at an intraday resolution. Demand in each intraday period is modeled using semiparametric regression smoothing to account for calendar and weather components. Residual serial dependence is captured by one of two multivariate stationary time series models, with dimension equal to the number of intraday periods. These are a periodic autoregression and a dynamic factor model. We show the benefits of our approach in the forecasting of district heating demand in a steam network in Germany and aggregate electricity demand in the state of Victoria, Australia. In both studies, accounting for weather …
Bayesian Approaches To Copula Modelling, Michael S. Smith
Bayesian Approaches To Copula Modelling, Michael S. Smith
Michael Stanley Smith
Copula models have become one of the most widely used tools in the applied modelling of multivariate data. Similarly, Bayesian methods are increasingly used to obtain efficient likelihood-based inference. However, to date, there has been only limited use of Bayesian approaches in the formulation and estimation of copula models. This article aims to address this shortcoming in two ways. First, to introduce copula models and aspects of copula theory that are especially relevant for a Bayesian analysis. Second, to outline Bayesian approaches to formulating and estimating copula models, and their advantages over alternative methods. Copulas covered include Archimedean, copulas constructed …
Time Series, Unit Roots, And Cointegration: An Introduction, Lonnie K. Stevans
Time Series, Unit Roots, And Cointegration: An Introduction, Lonnie K. Stevans
Lonnie K. Stevans
The econometric literature on unit roots took off after the publication of the paper by Nelson and Plosser (1982) that argued that most macroeconomic series have unit roots and that this is important for the analysis of macroeconomic policy. Yule (1926) suggested that regressions based on trending time series data can be spurious. This problem of spurious correlation was further pursued by Granger and Newbold (1974) and this also led to the development of the concept of cointegration (lack of cointegration implies spurious regression). The pathbreaking paper by Granger (1981), first presented at a conference at the University of Florida …
Statistical Methods For Proteomic Biomarker Discovery Based On Feature Extraction Or Functional Modeling Approaches, Jeffrey S. Morris
Statistical Methods For Proteomic Biomarker Discovery Based On Feature Extraction Or Functional Modeling Approaches, Jeffrey S. Morris
Jeffrey S. Morris
In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational …
Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do
Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do
Jeffrey S. Morris
Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current integration approaches that treat the data are limited in that they do not consider the fundamental biological relationships that exist among the data from platforms.
Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses a hierarchical modeling technique to combine the data obtained from multiple platforms …
A Comparative Analysis Of Decision Trees Vis-À-Vis Other Computational Data Mining Techniques In Automotive Insurance Fraud Detection, Adrian Gepp, Kuldeep Kumar, J Holton Wilson, Sukanto Bhattacharya
A Comparative Analysis Of Decision Trees Vis-À-Vis Other Computational Data Mining Techniques In Automotive Insurance Fraud Detection, Adrian Gepp, Kuldeep Kumar, J Holton Wilson, Sukanto Bhattacharya
Adrian Gepp
No abstract provided.
Modeling Dependence Using Skew T Copulas: Bayesian Inference And Applications, Michael S. Smith, Quan Gan, Robert Kohn
Modeling Dependence Using Skew T Copulas: Bayesian Inference And Applications, Michael S. Smith, Quan Gan, Robert Kohn
Michael Stanley Smith
[THIS IS AN AUGUST 2010 REVISION THAT REPLACES ALL PREVIOUS VERSIONS.]
We construct a copula from the skew t distribution of Sahu, Dey & Branco (2003). This copula can capture asymmetric and extreme dependence between variables, and is one of the few copulas that can do so and still be used in high dimensions effectively. However, it is difficult to estimate the copula model by maximum likelihood when the multivariate dimension is high, or when some or all of the marginal distributions are discrete-valued, or when the parameters in the marginal distributions and copula are estimated jointly. We therefore propose …