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Articles 31 - 60 of 525
Full-Text Articles in Physical Sciences and Mathematics
Longitudinal High-Dimensional Data Analysis, Vadim Zipunnikov, Sonja Greven, Brian Caffo, Daniel S. Reich, Ciprian Crainiceanu
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, Hong Zhu, Mei-Cheng Wang
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 …
Introducing Functional Data Analysis To Neuroimaging, And Vice Versa, Philip T. Reiss
Introducing Functional Data Analysis To Neuroimaging, And Vice Versa, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Likelihood Based Population Independent Component Analysis, Ani Eloyan, Ciprian M. Crainiceanu, Brian S. Caffo
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, Jeff Goldsmith, Sonja Greven, Ciprian M. Crainiceanu
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, Joseph W. Houpt, Robert D. Hawkins, Ami Eidels, James T. Townsend, Michael J. Wenger
Configuration As A Source Of Information, Joseph W. Houpt, Robert D. Hawkins, Ami Eidels, James T. Townsend, Michael J. Wenger
Joseph W. Houpt
No abstract provided.
Configuration As A Source Of Information, Joseph W. Houpt, Robert D. Hawkins, Ami Eidels, James T. Townsend, Michael J. Wenger
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.
Clinically Optimal Dosing In The Treatment Of Duodenal Ulcers: A Case Study Of A Phase Iii Snda Clinical Program, Karl E. Peace
Clinically Optimal Dosing In The Treatment Of Duodenal Ulcers: A Case Study Of A Phase Iii Snda Clinical Program, 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, Robert D. Hawkins, Joseph W. Houpt, Ami Eidels, James T. Townsend, Michael J. Wenger
Fundamental Properties Of Simple Emergent Feature Processing, Robert D. Hawkins, Joseph W. Houpt, Ami Eidels, James T. Townsend, Michael J. Wenger
Joseph W. Houpt
No abstract provided.
Fundamental Properties Of Simple Emergent Feature Processing, Robert D. Hawkins, Joseph W. Houpt, Ami Eidels, James T. Townsend, Michael J. Wenger
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.
Habitat Use And Abundance Patterns Of Sandhill Cranes In The Central Platte River Valley, Nebraska, 2003–2010, Todd Joseph Buckley
Habitat Use And Abundance Patterns Of Sandhill Cranes In The Central Platte River Valley, Nebraska, 2003–2010, Todd Joseph Buckley
School of Natural Resources: Dissertations, Theses, and Student Research
The Central Platte River Valley (CPRV) in Nebraska is an important spring stopover area for the midcontinent population of sandhill cranes. Alterations to crop rotation and loss habitat in the CPRV pose a risk to the population. Personnel drove designated routes in the CPRV from 2003–2010 to record the presence of cranes in agricultural fields and estimate abundance. I developed and evaluated models to predict habitat use and flock sizes. Alfalfa was predicted to receive the highest use followed by corn, soybeans, winter wheat, grassland, and shrubland. Use of all habitats and flock size increased as field area increased. Flock …
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
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.
A Comparison Of Factor Rotation Methods For Dichotomous Data, W. Holmes Finch
A Comparison Of Factor Rotation Methods For Dichotomous Data, W. Holmes Finch
Journal of Modern Applied Statistical Methods
Exploratory factor analysis (EFA) is frequently used in the social sciences and is a common component in many validity studies. A core aspect of EFA is the determination of which observed indicator variables are associated with which latent factors through the use of factor loadings. Loadings are initially extracted using an algorithm, such as maximum likelihood or weighted least squares, and then transformed - or rotated - to make them more interpretable. There are a number of rotational techniques available to the researcher making use of EFA. Prior work has discussed the advantages of a number of these criteria from …
Type I Error Rates Of The Two-Sample Pseudo-Median Procedure, Nor Aishah Ahad, Abdul Rahman Othman, Sharipah Soaad Syed Yahaya
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.
Robustness, Power And Interpretability Of Pairwise Tests Of Discriminant Functions In Manova, Philip H. Ramsey, Patricia P. Ramsey, Priscila Hachimine, Nancy Andiloro
Robustness, Power And Interpretability Of Pairwise Tests Of Discriminant Functions In Manova, Philip H. Ramsey, Patricia P. Ramsey, Priscila Hachimine, Nancy Andiloro
Journal of Modern Applied Statistical Methods
Limiting follow-up hypotheses to be tested can reduce problems relating to the control of Type I and Type II errors in multivariate analysis of variance (MANOVA). Such limitations can also improve the interpretability of results. The importance of sample size, shape of population distribution, within-group correlations and heterogeneity of variances are demonstrated. The protected greatest characteristic root (GCR) procedure is shown to work well for small, group size, N (≤ 10). The unprotected GCR is shown to work well for larger N.
Modified Ratio And Product Estimators For Population Mean In Systematic Sampling, Housila P. Singh, Rajesh Tailor, Narendra Kumar Jatwa
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.
Robust Inference For Regression With Spatially Correlated Errors, Juchi Ou, Jeffrey M. Albert
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.
Probabilistic Inferences For The Sample Pearson Product Moment Correlation, Jeffrey R. Harring, John A. Wasko
Probabilistic Inferences For The Sample Pearson Product Moment Correlation, Jeffrey R. Harring, John A. Wasko
Journal of Modern Applied Statistical Methods
Fisher’s correlation transformation is commonly used to draw inferences regarding the reliability of tests comprised of dichotomous or polytomous items. It is illustrated theoretically and empirically that omitting test length and difficulty results in inflated Type I error. An empirically unbiased correction is introduced within the transformation that is applicable under any test conditions.
Estimation Of Parameters Of Johnson’S System Of Distributions, Florence George, K. M. Ramachandran
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 …
Error Analysis On The Generalized Negative Binomial Distribution, Felix Famoye, Oluwakemi Aremu
Error Analysis On The Generalized Negative Binomial Distribution, Felix Famoye, Oluwakemi Aremu
Journal of Modern Applied Statistical Methods
The generalized negative binomial distribution characterized by three parameters, has been used to fit data from various fields of study. The distribution can model data for which the variance is larger or smaller than the mean, however, it becomes truncated under certain conditions. This truncation error is investigated via a detailed error analysis that determines the parameter space when the model can be used in place of the truncated generalized negative binomial distribution. The fitting of a generalized negative. K. M. Ramachandran is a
Ordinal Regression Analysis: Predicting Mathematics Proficiency Using The Continuation Ratio Model, Xing Liu, Ann A. O'Connell, Hari Koirala
Ordinal Regression Analysis: Predicting Mathematics Proficiency Using The Continuation Ratio Model, Xing Liu, Ann A. O'Connell, Hari Koirala
Journal of Modern Applied Statistical Methods
One commonly used model to analyze ordinal response data is the proportional odds (PO) model. However, if research interest is focused on a particular category and if an individual must pass through lower categories before achieving a higher level, the continuation ratio (CR) model is a more appropriate choice than the PO model. In addition, statistical software, such as Stata and SAS, may use different techniques to estimate the parameters. The CR model is used to illustrate the analysis of ordinal data in education using Stata and SAS and compares the results of fitting the CR model between these two …
Estimation And Hypothesis Testing In Lav Regression With Autocorrelated Errors: Is Correction For Autocorrelation Helpful?, Terry E. Dielman
Estimation And Hypothesis Testing In Lav Regression With Autocorrelated Errors: Is Correction For Autocorrelation Helpful?, Terry E. Dielman
Journal of Modern Applied Statistical Methods
Using the Prais-Winsten correction and adding a lagged variable provides improved estimates (smaller MSE) in least absolute value (LAV) regression when moderate to high levels of autocorrelation are present. When comparing empirical levels of significance for hypothesis tests, adding a lagged variable outperforms other approaches but has a relative high empirical level of significance.
Maximum Log Likelihood Estimation Using Em Algorithm And Partition Maximum Log Likelihood Estimation For Mixtures Of Generalized Lambda Distributions, Steve Su
Journal of Modern Applied Statistical Methods
Two mixture distribution fitting methods based on maximizing the likelihood using generalized lambda distributions are presented. The fitting algorithms are demonstrated on various data and the strengths and weakness of the algorithms which can influence their use under different mixture modeling situations are discussed. The procedures described are available in GLDEX package in R.
On Maximum Likelihood Estimators Of The Parameters Of A Modified Weibull Distribution Using Extreme Ranked Set Sampling, Amer Ibrahim Al-Omari, Said Ali Al-Hadhrami
On Maximum Likelihood Estimators Of The Parameters Of A Modified Weibull Distribution Using Extreme Ranked Set Sampling, Amer Ibrahim Al-Omari, Said Ali Al-Hadhrami
Journal of Modern Applied Statistical Methods
Extreme ranked set sampling (ERSS) is considered to estimate the three parameters and population mean of the modified Weibull distribution (MWD). The maximum likelihood estimator (MLE) is investigated and compared to the corresponding one based on simple random sampling (SRS). It is found that, the MLE based on ERSS is more efficient than MLE using SRS for estimating the three parameters of the MWD. The ERSS estimator of the population mean of the MWD is also found to be more efficient than the SRS based on the same number of measured units.
Indeterminacy Of Factor Score Estimates In Slightly Misspecified Confirmatory Factor Models, André Beauducel
Indeterminacy Of Factor Score Estimates In Slightly Misspecified Confirmatory Factor Models, André Beauducel
Journal of Modern Applied Statistical Methods
Two methods to calculate a measure for the quality of factor score estimates have been proposed. These methods were compared by means of a simulation study. The method based on a covariance matrix reproduced from a model leads to smaller effects of sampling error.
Modeling Repairable System Failures With Interval Failure Data And Time Dependent Covariate, Jayanthi Arasan, Samira Ehsani
Modeling Repairable System Failures With Interval Failure Data And Time Dependent Covariate, Jayanthi Arasan, Samira Ehsani
Journal of Modern Applied Statistical Methods
An application of a repairable system model for interval failure data with a time dependent covariate is examined. The performance of several models based on the NHPP when applied to real data on ball bearing failures is also explored. The best model for the data was selected based on results of the likelihood ratio test. The bootstrapping technique was applied to obtain the variance estimate for the estimated expected number of failures. Results demonstrate that the proposed model works well and is easy to implement, in addition the bootstrap variance estimate provides a simple substitute for the traditional estimate.
Non-Homogenous Poisson Process For Evaluating Stage I & Ii Ductal Breast Cancer Treatment, Chris P. Tsokos, Yong Xu
Non-Homogenous Poisson Process For Evaluating Stage I & Ii Ductal Breast Cancer Treatment, Chris P. Tsokos, Yong Xu
Journal of Modern Applied Statistical Methods
Non-Homogenous Poisson Process (NHPP), also known as the Power Law process (PLP) or the Weibull Process, is used to evaluate the effectiveness of a given treatment for Stage I & II ductal breast cancer patients. The behavior of the shape parameter of the intensity function is examined to evaluate the response of a given treatment with respect to its effectiveness for a cancer subject.
Salary Equity Studies: An Analysis Of Using The Blinder-Oaxaca Decomposition To Estimate Differences In Faculty Salaries By Gender, Sally A. Lesik, Carolyn R. Fallahi
Salary Equity Studies: An Analysis Of Using The Blinder-Oaxaca Decomposition To Estimate Differences In Faculty Salaries By Gender, Sally A. Lesik, Carolyn R. Fallahi
Journal of Modern Applied Statistical Methods
Parameter estimates for equity studies tested for stability are described. Bootstrap simulation can test whether parameter estimates remain stable given changes in the sample data; fractional polynomials can be used to access functional form specification; and variance inflation factors can be used to test for multicollinearity.
A Sequential Monte Carlo Approach For Online Stock Market Prediction Using Hidden Markov Models, Ahani E. Bridget, O. Abass
A Sequential Monte Carlo Approach For Online Stock Market Prediction Using Hidden Markov Models, Ahani E. Bridget, O. Abass
Journal of Modern Applied Statistical Methods
A sequential Monte Carlo (SMC) algorithm prediction approach is developed based on joint probability distribution in hidden Markov Models (HMM). SMC methods, a general class of Monte Carlo methods, are typically used for sampling from sequences of distributions and simple examples of these algorithms are found extensively throughout the tracking and signal processing literature. Recent developments indicate that these techniques have much more general applicability and can be applied very effectively to statistical inference problems. Due to the problem involved in estimating the parameter of HMM, the HMM is represented in a state space model and the sequential Monte Carlo …
A Pooled Two-Sample Median Test Based On Density Estimation, Vadim Y. Bichutskiy
A Pooled Two-Sample Median Test Based On Density Estimation, Vadim Y. Bichutskiy
Journal of Modern Applied Statistical Methods
A new method based on density estimation is proposed for medians of two independent samples. The test controls the probability of Type I error and is at least as powerful as methods widely used in statistical practice. The method can be implemented using existing libraries in R.