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Longitudinal Data Analysis and Time Series Commons™
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- And easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals (1)
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- Bayesian; multi-state; recurrent event; competing risk; hierarchical; stratified; survival analysis This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible (1)
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Articles 1 - 16 of 16
Full-Text Articles in Longitudinal Data Analysis and Time Series
Modeling Multilevel Sleep Transitional Data Via Poisson Log-Linear Multilevel Models, Bruce J. Swihart, Brian Caffo, Ciprian Crainiceanu, Naresh M. Punjabi
Modeling Multilevel Sleep Transitional Data Via Poisson Log-Linear Multilevel Models, Bruce J. Swihart, Brian Caffo, Ciprian Crainiceanu, Naresh M. Punjabi
Johns Hopkins University, Dept. of Biostatistics Working Papers
This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified …
Sequence Comparison And Stochastic Model Based On Multi-Order Markov Models, Xiang Fang
Sequence Comparison And Stochastic Model Based On Multi-Order Markov Models, Xiang Fang
Department of Statistics: Dissertations, Theses, and Student Work
This dissertation presents two statistical methodologies developed on multi-order Markov models. First, we introduce an alignment-free sequence comparison method, which represents a sequence using a multi-order transition matrix (MTM). The MTM contains information of multi-order dependencies and provides a comprehensive representation of the heterogeneous composition within a sequence. Based on the MTM, a distance measure is developed for pair-wise comparison of sequences. The new method is compared with the traditional maximum likelihood (ML) method, the complete composition vector (CCV) method and the improved version of the complete composition vector (ICCV) method using simulated sequences. We further illustrate the application of …
Lasagna Plots: A Saucy Alternative To Spaghetti Plots, Bruce Swihart, Brian Caffo, Bryan D. James, Matthew Strand, Brian S. Schwartz, Naresh M. Punjabi
Lasagna Plots: A Saucy Alternative To Spaghetti Plots, Bruce Swihart, Brian Caffo, Bryan D. James, Matthew Strand, Brian S. Schwartz, Naresh M. Punjabi
Johns Hopkins University, Dept. of Biostatistics Working Papers
Longitudinal repeated measures data has often been visualized with spaghetti plots for continuous out- comes. For large datasets, this often leads to over-plotting and consequential obscuring of trends in the data. This is primarily due to overlapping of trajectories. Here, we suggest a framework called lasagna plot ting that constrains the subject-specific trajectories to prevent overlapping and utilizes gradients of color to depict the outcome. Dynamic sorting and visualization is demonstrated as an exploratory data analysis tool. Supplemental material in the form of sample R code additional illustrated examples are available online.
Modeling Multilevel Sleep Transitional Data Via Poisson Log-Linear Multilevel Models, Bruce J. Swihart
Modeling Multilevel Sleep Transitional Data Via Poisson Log-Linear Multilevel Models, Bruce J. Swihart
COBRA Preprint Series
This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified …
Reliability Of The Model For Clustering Of Longitudinal Datasets Of Infant Mortality Rate In India, Ajay Kumar Bansal, S D. Sharma
Reliability Of The Model For Clustering Of Longitudinal Datasets Of Infant Mortality Rate In India, Ajay Kumar Bansal, S D. Sharma
COBRA Preprint Series
Because of the natural tendency of human beings and heavenly bodies to form groups, the technique of cluster analysis or segmentation analysis find its importance and applications in many fields of study. A model for clustering of time trends was proposed by authors whose beauty is that 2-way dimensions that is the horizontal flow of the trend and vertical distance of the trend from a common base are considered to obtain the natural clusters. In the present paper, the reliability of this model is studied in two steps namely (i) by repeating the analysis but using different interval distance measures …
Time Series Analysis, Yogendra Sisodia
Time Series Analysis, Yogendra Sisodia
Yogendra Sisodia
Time Series Analysis using Project R. ARMA Model on Nifty Spot, Nifty Futures and MCX Comdex.
Time Valuation Of Risk A Delayed-Bang Approach, Abhishek Pathak
Time Valuation Of Risk A Delayed-Bang Approach, Abhishek Pathak
Engineering Management & Systems Engineering Theses & Dissertations
The subject of this thesis is the combined use of engineering economics and survival analysis in estimating time-value of risk-related resources. The discussion includes (1) the need for sustainable risk management, (2) the importance of time-valuation of risk related resources in the allocation or selection among competing risk mitigation alternatives, (3) the convergence of deterministic engineering economics, survivability analysis, and probabilistic analysis, and (4) results and examples of application in the context of prevention of risk event or mitigation of its consequences.
The significance of this thesis is in how three topics: engineering economics, survivability analysis, and probability theory can …
Spatial Cluster Detection For Repeatedly Measured Outcomes While Accounting For Residential History, Andrea J. Cook, Diane Gold, Yi Li
Spatial Cluster Detection For Repeatedly Measured Outcomes While Accounting For Residential History, Andrea J. Cook, Diane Gold, Yi Li
Harvard University Biostatistics Working Paper Series
No abstract provided.
Spatial Cluster Detection For Weighted Outcomes Using Cumulative Geographic Residuals, Andrea J. Cook, Yi Li, David Arterburn, Ram C. Tiwari
Spatial Cluster Detection For Weighted Outcomes Using Cumulative Geographic Residuals, Andrea J. Cook, Yi Li, David Arterburn, Ram C. Tiwari
Harvard University Biostatistics Working Paper Series
No abstract provided.
Smoking Enhances Risk For New External Genital Warts In Men, Dorothy J. Wiley, David Elashoff, Emmanuel V. Masongsong, Diane M. Harper
Smoking Enhances Risk For New External Genital Warts In Men, Dorothy J. Wiley, David Elashoff, Emmanuel V. Masongsong, Diane M. Harper
Dartmouth Scholarship
Repeat episodes of HPV-related external genital warts reflect recurring or new infections. No study before has been sufficiently powered to delineate how tobacco use, prior history of EGWs and HIV infection affect the risk for new EGWs. Behavioral, laboratory and examination data for 2,835 Multicenter AIDS Cohort Study participants examined at 21,519 semi-annual visits were evaluated. Fourteen percent (391/2835) of men reported or were diagnosed with EGWs at 3% (675/21,519) of study visits. Multivariate analyses showed smoking, prior episodes of EGWs, HIV infection and CD4+ T-lymphocyte count among the infected, each differentially influenced the risk for new EGWs.
Corso Di Analisi Delle Serie Storiche A.A 2008/2009- Laboratorio Di Stata: Lezione 4 –Analisi Classica Delle Serie Storiche Ii (Formato Pdf), Carlo Drago
Carlo Drago
No abstract provided.
Corso Di Analisi Delle Serie Storiche A.A 2008/2009- Laboratorio Di Stata: Lezione 5 –Analisi Moderna Delle Serie Storiche (Formato Pdf), Carlo Drago
Carlo Drago
No abstract provided.
Multilevel Functional Principal Component Analysis, Chong-Zhi Di, Ciprian M. Crainiceanu, Brian S. Caffo, Naresh M. Punjabi
Multilevel Functional Principal Component Analysis, Chong-Zhi Di, Ciprian M. Crainiceanu, Brian S. Caffo, Naresh M. Punjabi
Chongzhi Di
The Sleep Heart Health Study (SHHS) is a comprehensive landmark study of sleep and its impacts on health outcomes. A primary metric of the SHHS is the in-home polysomnogram, which includes two electroencephalographic (EEG) channels for each subject, at two visits. The volume and importance of this data presents enormous challenges for analysis. To address these challenges, we introduce multilevel functional principal component analysis (MFPCA), a novel statistical methodology designed to extract core intra- and inter-subject geometric components of multilevel functional data. Though motivated by the SHHS, the proposed methodology is generally applicable, with potential relevance to many modern scientific …
Nonparametric Signal Extraction And Measurement Error In The Analysis Of Electroencephalographic Activity During Sleep, Ciprian M. Crainiceanu, Brian S. Caffo, Chong-Zhi Di, Naresh M. Punjabi
Nonparametric Signal Extraction And Measurement Error In The Analysis Of Electroencephalographic Activity During Sleep, Ciprian M. Crainiceanu, Brian S. Caffo, Chong-Zhi Di, Naresh M. Punjabi
Chongzhi Di
We introduce methods for signal and associated variability estimation based on hierarchical nonparametric smoothing with application to the Sleep Heart Health Study (SHHS). SHHS is the largest electroencephalographic (EEG) collection of sleep-related data, which contains, at each visit, two quasi-continuous EEG signals for each subject. The signal features extracted from EEG data are then used in second level analyses to investigate the relation between health, behavioral, or biometric outcomes and sleep. Using subject specific signals estimated with known variability in a second level regression becomes a nonstandard measurement error problem.We propose and implement methods that take into account cross-sectional and …
Generalized Multilevel Functional Regression, Ciprian M. Crainiceanu, Ana-Maria Staicu, Chong-Zhi Di
Generalized Multilevel Functional Regression, Ciprian M. Crainiceanu, Ana-Maria Staicu, Chong-Zhi Di
Chongzhi Di
We introduce Generalized Multilevel Functional Linear Models (GMFLMs), a novel statistical framework for regression models where exposure has a multilevel functional structure. We show that GMFLMs are, in fact, generalized multilevel mixed models. Thus, GMFLMs can be analyzed using the mixed effects inferential machinery and can be generalized within a well-researched statistical framework. We propose and compare two methods for inference: (1) a two-stage frequentist approach; and (2) a joint Bayesian analysis. Our methods are motivated by and applied to the Sleep Heart Health Study, the largest community cohort study of sleep. However, our methods are general and easy to …
Syllabus Of Mathematics For Economists (Master's Course), Reza Moosavi Mohseni Dr.
Syllabus Of Mathematics For Economists (Master's Course), Reza Moosavi Mohseni Dr.
Reza Moosavi Mohseni
No abstract provided.