Longitudinal High-Dimensional Data Analysis,
2011
Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
Longitudinal High-Dimensional Data Analysis, Vadim Zipunnikov, Sonja Greven, Brian Caffo, Daniel S. Reich, Ciprian Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
We develop a flexible framework for modeling high-dimensional functional and imaging data observed longitudinally. The approach decomposes the observed variability of high-dimensional observations measured at multiple visits into three additive components: a subject-specific functional random intercept that quantifies the cross-sectional variability, a subject-specific functional slope that quantifies the dynamic irreversible deformation over multiple visits, and a subject-visit specific functional deviation that quantifies exchangeable or reversible visit-to-visit changes. The proposed method is very fast, scalable to studies including ultra-high dimensional data, and can easily be adapted to and executed on modest computing infrastructures. The method is applied to the longitudinal analysis …
Assessing Association For Bivariate Survival Data With Interval Sampling: A Copula Model Approach With Application To Aids Study,
2011
The Ohio State University
Assessing Association For Bivariate Survival Data With Interval Sampling: A Copula Model Approach With Application To Aids Study, Hong Zhu, Mei-Cheng Wang
Johns Hopkins University, Dept. of Biostatistics Working Papers
In disease surveillance systems or registries, bivariate survival data are typically collected under interval sampling. It refers to a situation when entry into a registry is at the time of the first failure event (e.g., HIV infection) within a calendar time interval, the time of the initiating event (e.g., birth) is retrospectively identified for all the cases in the registry, and subsequently the second failure event (e.g., death) is observed during the follow-up. Sampling bias is induced due to the selection process that the data are collected conditioning on the first failure event occurs within a time interval. Consequently, the …
Likelihood Based Population Independent Component Analysis,
2011
Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
Likelihood Based Population Independent Component Analysis, Ani Eloyan, Ciprian M. Crainiceanu, Brian S. Caffo
Johns Hopkins University, Dept. of Biostatistics Working Papers
Independent component analysis (ICA) is a widely used technique for blind source separation, used heavily in several scientific research areas including acoustics, electrophysiology and functional neuroimaging. We propose a scalable two-stage iterative true group ICA methodology for analyzing population level fMRI data where the number of subjects is very large. The method is based on likelihood estimators of the underlying source densities and the mixing matrix. As opposed to many commonly used group ICA algorithms the proposed method does not require significant data reduction by a twofold singular value decomposition. In addition, the method can be applied to a large …
Corrected Confidence Bands For Functional Data Using Principal Components,
2011
Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
Corrected Confidence Bands For Functional Data Using Principal Components, Jeff Goldsmith, Sonja Greven, Ciprian M. Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this paper, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- based and decomposition-based variability are constructed. Standard mixed-model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. A bootstrap procedure is implemented to understand the uncertainty in …
Configuration As A Source Of Information,
2011
Wright State University - Main Campus
Configuration As A Source Of Information, Joseph W. Houpt, Robert D. Hawkins, Ami Eidels, James T. Townsend, Michael J. Wenger
Psychology Faculty Publications
No abstract provided.
Case Study In Optimal Dosing In Duodenal Ulcer,
2011
Georgia Southern University
Case Study In Optimal Dosing In Duodenal Ulcer, Karl E. Peace
Biostatistics: Faculty Publications
Georgia Southern University faculty member Karl E. Peace authored "Case Study in Optimal Dosing in Duodenal Eulcer" in Peptic Ulcer Disease.
Fundamental Properties Of Simple Emergent Feature Processing,
2011
Wright State University - Main Campus
Fundamental Properties Of Simple Emergent Feature Processing, Robert D. Hawkins, Joseph W. Houpt, Ami Eidels, James T. Townsend, Michael J. Wenger
Psychology Faculty Publications
No abstract provided.
Calibration Systems And Methods For Infrared Cameras,
2011
Goleta, CA
Calibration Systems And Methods For Infrared Cameras, Russell Granneman, Nuwan Nagahawatte, Richard M. Goeden, Ted Takagi, Robert Ernst, Gary B. Hughes, Joseph Kostrzewa, John Graff, George Speake, Michael Kent, Neela Nalam, Stephen Lyon, Barbara Sharp, Pierre Boulanger, Neil Cutcliffe, Tim Martin, Ted Hoelter
Statistics
Systems and methods directed to calibration techniques for infrared cameras are disclosed. For example, a method of obtaining calibration information for an infrared device includes providing a calibration target adapted to provide a low-emissivity scene; performing a calibration operation on the infrared device to obtain the calibration information; and storing the calibration information.
Comparison Of Several Tests For Combining Several Independent Tests,
2011
Columbus State University
Comparison Of Several Tests For Combining Several Independent Tests, Madhusudan Bhandary, Xuan Zhang
Journal of Modern Applied Statistical Methods
Several tests for combining p-values from independent tests have been considered to address a particular common testing problem. A simulation study shows that Fisher’s (1932) Inverse Chi-square test is optimal based on a power comparison of several different tests.
Discriminant Analysis For Repeated Measures Data: Effects Of Mean And Covariance Misspecification On Bias And Error In Discriminant Function Coefficients,
2011
University of Saskatchewan
Discriminant Analysis For Repeated Measures Data: Effects Of Mean And Covariance Misspecification On Bias And Error In Discriminant Function Coefficients, Tolulope T. Sajobi, Lisa M. Lix, Longhai Li, William Laverty
Journal of Modern Applied Statistical Methods
Discriminant analysis (DA) procedures based on parsimonious mean and/or covariance structures have been proposed for repeated measures (RM) data. Bias and means square error of discriminant function coefficients (DFCs) for DA procedures are investigated when the mean and/or covariance structures are correctly specified and misspecified.
Construction Of Control Charts Based On Six Sigma Initiatives For The Number Of Defects And Average Number Of Defects Per Unit,
2011
P.S.G. College of Arts and Science
Construction Of Control Charts Based On Six Sigma Initiatives For The Number Of Defects And Average Number Of Defects Per Unit, R. Radhakrishnan, P. Balamurugan
Journal of Modern Applied Statistical Methods
A control chart is a statistical device used for the study and control of a repetitive process. In 1931, Shewart suggested control charts based on 3 sigma limits. Today manufacturing companies around the world apply Six Sigma initiatives, with a result offewer product defects. Companies practicing Six Sigma initiatives are expected to produce 3.4 or less number of defects per million opportunities, a concept suggested by Motorola in 1980. If companies practicing Six Sigma initiatives use control limits suggested by Shewhart, then no points will fall outside the control limits due to the improvement in the quality of the process. …
Identification Of Optimal Autoregressive Integrated Moving Average Model On Temperature Data,
2011
Federal University of Technology
Identification Of Optimal Autoregressive Integrated Moving Average Model On Temperature Data, Olusola Samuel Makinde, Olusoga Akin Fasoranbaku
Journal of Modern Applied Statistical Methods
Autoregressive Integrated Moving Average (ARIMA) processes of various orders are presented to identify an optimal model from a class of models. Parameters of the models are estimated using an Ordinary Least Square (OLS) approach. ARIMA (p, d, q) is formulated for maximum daily temperature data in Ondo and Zaira from January 1995 to November 2005. The choice of ARIMA models of orders p and q is intended to retain persistence in a natural process. To determine the performance of models, Normalized Bayesian Information Criterion is adopted. The ARIMA (1, 1, 1) is adequate for modeling maximum daily temperature in Ondo …
Height-Diameter Relationship In Tree Modeling Using Simultaneous Equation Techniques In Correlated Normal Deviates,
2011
Forestry Research Institute of Nigeria
Height-Diameter Relationship In Tree Modeling Using Simultaneous Equation Techniques In Correlated Normal Deviates, S. O. Oyamakin
Journal of Modern Applied Statistical Methods
In other to study the complex simultaneous relationships existing in forest/tree growth modeling, six estimation methods of a simultaneous equation model are examined to determine how they cope with varying degrees of correlation between pairs of random deviates using average parameter estimates. A two-equation simultaneous system assumed covariance matrix was considered. The model was structured to have a mutual correlation between pairs of random deviates: a violation of the assumption of mutual independence between pairs of such random deviates. The correlation between the pairs of normal deviates were generated using three scenarios r = 0.0, 0.3 and 0.5. The performances …
Tests For Correlation On Bivariate Non-Normal Data,
2011
North Carolina State University
Tests For Correlation On Bivariate Non-Normal Data, L. Beversdorf, Ping Sa
Journal of Modern Applied Statistical Methods
Two statistics are considered to test the population correlation for non-normally distributed bivariate data. A simulation study shows that both statistics control type I error rates well for left-tailed tests and have reasonable power performance.
Lq-Moments For Regional Flood Frequency Analysis: A Case Study For The North-Bank Region Of The Brahmaputra River, India,
2011
Tezpur University
Lq-Moments For Regional Flood Frequency Analysis: A Case Study For The North-Bank Region Of The Brahmaputra River, India, Abhijit Bhuyan, Munindra Borah
Journal of Modern Applied Statistical Methods
The LQ-moment proposed by Mudholkar, et al. (1998) is used for regional flood frequency analysis of the North-Bank region of the river Brahmaputra, India. Five probability distributions are used for the LQmoment: generalized extreme value (GEV), generalized logistic (GLO) and generalized Pareto (GPA), lognormal (LN3) and Pearson Type III (PE3). The same regional frequency analysis procedure proposed by Hosking (1990) for the L-moment is used for the LQ-moment. Based on the LQ-moment ratio diagram and |Zidist| -statistic criteria, the PE3 distribution is identified as the robust distribution for the study area. For estimation of floods of various …
Explicit Equations For Acf In Autoregressive Processes In The Presence Of Heteroscedasticity Disturbances,
2011
The Islamic University of Gaza
Explicit Equations For Acf In Autoregressive Processes In The Presence Of Heteroscedasticity Disturbances, Samir Safi
Journal of Modern Applied Statistical Methods
The autocorrelation function, ACF, is an important guide to the properties of a time series. Explicit equations are derived for ACF in the presence of heteroscedasticity disturbances in pth order autoregressive, AR(p), processes. Two cases are presented: (1) when the disturbance term follows the general covariance matrix, Σ , and (2) when the diagonal elements of Σ are not all identical but σi,j = 0 ∀i ≠ j.
Type I Error Rates Of The Two-Sample Pseudo-Median Procedure,
2011
Universiti Utara Malaysia
Type I Error Rates Of The Two-Sample Pseudo-Median Procedure, Nor Aishah Ahad, Abdul Rahman Othman, Sharipah Soaad Syed Yahaya
Journal of Modern Applied Statistical Methods
The performance of the pseudo-median based procedure is examined in terms of controlling Type I error for a two independent groups test. The procedure is a modification of the one-sample Wilcoxon statistic using the pseudo-median of differences between group values as the central measure of location. The proposed procedure was shown to have good control of Type I error rates under the study conditions regardless of distribution type.
Modified Ratio And Product Estimators For Population Mean In Systematic Sampling,
2011
Vikram University, Ujjain, India
Modified Ratio And Product Estimators For Population Mean In Systematic Sampling, Housila P. Singh, Rajesh Tailor, Narendra Kumar Jatwa
Journal of Modern Applied Statistical Methods
The estimation of population mean in systematic sampling is explored. Properties of a ratio and product estimator that have been suggested in systematic sampling are investigated, along with the properties of double sampling. Following Swain (1964), the cost aspect is also discussed.
Estimation Of Parameters Of Johnson’S System Of Distributions,
2011
Florida International University
Estimation Of Parameters Of Johnson’S System Of Distributions, Florence George, K. M. Ramachandran
Journal of Modern Applied Statistical Methods
Fitting distributions to data has a long history and many different procedures have been advocated. Although models like normal, log-normal and gamma lead to a wide variety of distribution shapes, they do not provide the degree of generality that is frequently desirable (Hahn & Shapiro, 1967). To formally represent a set of data by an empirical distribution, Johnson (1949) derived a system of curves with the flexibility to cover a wide variety of shapes. Methods available to estimate the parameters of the Johnson distribution are discussed, and a new approach to estimate the four parameters of the Johnson family is …
Robust Inference For Regression With Spatially Correlated Errors,
2011
Case Western Reserve University
Robust Inference For Regression With Spatially Correlated Errors, Juchi Ou, Jeffrey M. Albert
Journal of Modern Applied Statistical Methods
A robust variance estimator for a regression model with spatially correlated errors is proposed using the estimated empirical covariogram. Simulations studies show unbiasedness and robustness for the OLS but not for the GLS estimates. The new robust variance estimation method is applied to hospital quality data.. Stephanie A.
