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Articles 1 - 21 of 21
Full-Text Articles in Multivariate Analysis
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 …
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.
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 …
Identifying Unique Neighborhood Characteristics To Guide Health Planning For Stroke And Heart Attack: Fuzzy Cluster And Discriminant Analyses Approaches, Ashley Pedigo, William Seaver, Agricola Odoi
Identifying Unique Neighborhood Characteristics To Guide Health Planning For Stroke And Heart Attack: Fuzzy Cluster And Discriminant Analyses Approaches, Ashley Pedigo, William Seaver, Agricola Odoi
Agricola Odoi
Background: Socioeconomic, demographic, and geographic factors are known determinants of stroke and myocardial infarction (MI) risk. Clustering of these factors in neighborhoods needs to be taken into consideration during planning, prioritization and implementation of health programs intended to reduce disparities. Given the complex and multidimensional nature of these factors, multivariate methods are needed to identify neighborhood clusters of these determinants so as to better understand the unique neighborhood profiles. This information is critical for evidence-based health planning and service provision. Therefore, this study used a robust multivariate approach to classify neighborhoods and identify their socio-demographic characteristics so as to provide …
Cv, Lorán Chollete
International Diversification: An Extreme Value Approach, Lorán Chollete, Victor De La Peña, Ching-Chih Lu
International Diversification: An Extreme Value Approach, Lorán Chollete, Victor De La Peña, Ching-Chih Lu
Lorán Chollete
No abstract provided.
International Diversification: A Copula Approach, Lorán Chollete, Victor De La Pena, Ching-Chih Lu
International Diversification: A Copula Approach, Lorán Chollete, Victor De La Pena, Ching-Chih Lu
Lorán Chollete
No abstract provided.
Wavelet-Based Functional Linear Mixed Models: An Application To Measurement Error–Corrected Distributed Lag Models, Elizabeth J. Malloy, Jeffrey S. Morris, Sara D. Adar, Helen Suh, Diane R. Gold, Brent A. Coull
Wavelet-Based Functional Linear Mixed Models: An Application To Measurement Error–Corrected Distributed Lag Models, Elizabeth J. Malloy, Jeffrey S. Morris, Sara D. Adar, Helen Suh, Diane R. Gold, Brent A. Coull
Jeffrey S. Morris
Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient …
Members’ Discoveries: Fatal Flaws In Cancer Research, Jeffrey S. Morris
Members’ Discoveries: Fatal Flaws In Cancer Research, Jeffrey S. Morris
Jeffrey S. Morris
A recent article published in The Annals of Applied Statistics (AOAS) by two MD Anderson researchers—Keith Baggerly and Kevin Coombes—dissects results from a highly-influential series of medical papers involving genomics-driven personalized cancer therapy, and outlines a series of simple yet fatal flaws that raises serious questions about the veracity of the original results. Having immediate and strong impact, this paper, along with related work, is providing the impetus for new standards of reproducibility in scientific research.
Statistical Contributions To Proteomic Research, Jeffrey S. Morris, Keith A. Baggerly, Howard B. Gutstein, Kevin R. Coombes
Statistical Contributions To Proteomic Research, Jeffrey S. Morris, Keith A. Baggerly, Howard B. Gutstein, Kevin R. Coombes
Jeffrey S. Morris
Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the …
Informatics And Statistics For Analyzing 2-D Gel Electrophoresis Images, Andrew W. Dowsey, Jeffrey S. Morris, Howard G. Gutstein, Guang Z. Yang
Informatics And Statistics For Analyzing 2-D Gel Electrophoresis Images, Andrew W. Dowsey, Jeffrey S. Morris, Howard G. Gutstein, Guang Z. Yang
Jeffrey S. Morris
Whilst recent progress in ‘shotgun’ peptide separation by integrated liquid chromatography and mass spectrometry (LC/MS) has enabled its use as a sensitive analytical technique, proteome coverage and reproducibility is still limited and obtaining enough replicate runs for biomarker discovery is a challenge. For these reasons, recent research demonstrates the continuing need for protein separation by two-dimensional gel electrophoresis (2-DE). However, with traditional 2-DE informatics, the digitized images are reduced to symbolic data though spot detection and quantification before proteins are compared for differential expression by spot matching. Recently, a more robust and automated paradigm has emerged where gels are directly …
Bayesian Random Segmentationmodels To Identify Shared Copy Number Aberrations For Array Cgh Data, Veerabhadran Baladandayuthapani, Yuan Ji, Rajesh Talluri, Luis E. Nieto-Barajas, Jeffrey S. Morris
Bayesian Random Segmentationmodels To Identify Shared Copy Number Aberrations For Array Cgh Data, Veerabhadran Baladandayuthapani, Yuan Ji, Rajesh Talluri, Luis E. Nieto-Barajas, Jeffrey S. Morris
Jeffrey S. Morris
Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimation of the underlying copy number state at each location and segmenting regions of DNA with similar copy number states. Most current methods proceed by modeling a single sample/array at a time, and thus fail to borrow strength across multiple samples to infer shared regions of copy number aberrations. …
Financial Distress And Idiosyncratic Volatility: An Empirical Investigation, Lorán Chollete, Jing Chen, Rina Ray
Financial Distress And Idiosyncratic Volatility: An Empirical Investigation, Lorán Chollete, Jing Chen, Rina Ray
Lorán Chollete
No abstract provided.
Financial Implications Of Extreme And Rare Events, Lorán Chollete, Dwight Jaffee
Financial Implications Of Extreme And Rare Events, Lorán Chollete, Dwight Jaffee
Lorán Chollete
No abstract provided.
Dependence Of Macro Variables In The Us Economy, Lorán Chollete, Cathy Ning
Dependence Of Macro Variables In The Us Economy, Lorán Chollete, Cathy Ning
Lorán Chollete
No abstract provided.
Modeling International Financial Returns With A Multivariate Regime-Switching Copula, Lorán Chollete, Andreas Heinen, Alfonso Valdesogo
Modeling International Financial Returns With A Multivariate Regime-Switching Copula, Lorán Chollete, Andreas Heinen, Alfonso Valdesogo
Lorán Chollete
No abstract provided.
Economic Implications Of Copulas And Extremes, Lorán Chollete
Economic Implications Of Copulas And Extremes, Lorán Chollete
Lorán Chollete
No abstract provided.
Book Review: Reasoning Agents In A Dynamic World: The Frame Problem. Kenneth M. Ford And Patrick J. Hayes, Eds.,, Jozsef A. Toth
Book Review: Reasoning Agents In A Dynamic World: The Frame Problem. Kenneth M. Ford And Patrick J. Hayes, Eds.,, Jozsef A. Toth
Jozsef A Toth Ph.D.
No abstract provided.