Digital Commons Network^™

Identifying Important Explanatory Variables For Time-Varying Outcomes., Oliver Bembom, Maya L. Petersen, Mark J. Van Der Laan

Maya Petersen

This chapter describes a systematic and targeted approach for estimating the impact of each of a large number of baseline covariates on an outcome that is measured repeatedly over time. These variable importance estimates can be adjusted for a user-specified set of confounders and lend themselves in a straightforward way to obtaining confidence intervals and p-values. Hence, they can in particular be used to identify a subset of baseline covariates that are the most important explanatory variables for the time-varying outcome of interest. We illustrate the methodology in a data analysis aimed at finding mutations of the human immunodeficiency virus …

Identifying Important Explanatory Variables For Time-Varying Outcomes., Oliver Bembom, Maya L. Petersen, Mark J. Van Der Laan

Oliver Bembom

This chapter describes a systematic and targeted approach for estimating the impact of each of a large number of baseline covariates on an outcome that is measured repeatedly over time. These variable importance estimates can be adjusted for a user-specified set of confounders and lend themselves in a straightforward way to obtaining confidence intervals and p-values. Hence, they can in particular be used to identify a subset of baseline covariates that are the most important explanatory variables for the time-varying outcome of interest. We illustrate the methodology in a data analysis aimed at finding mutations of the human immunodeficiency virus …

Patterns And Dynamics Of Ocean Circulation Variability On The West Florida Shelf, Yonggang Liu

USF Tampa Graduate Theses and Dissertations

Patterns of variability and the dynamics of the ocean circulation on the West Florida Shelf (WFS) are investigated using multi-year, shelf-wide oceanographic observations from moored Acoustic Doppler Current Profiler (ADCP) arrays,hydrographic cruises, High-Frequency (HF) radars, satellites, and coastal tide gauges.Novel neural network techniques, Self-Organizing Map (SOM) and Growing Hierarchical Self-Organizing Maps (GHSOM), are introduced as feature extraction methods in physical oceanography. The SOM is demystified and demonstrated to be a useful feature extraction method in a series of performance evaluations using artificial data sets comprising known patterns. It is then applied to velocity time series from moored ADCP arrays and …

Properties Of The Gar(1) Model For Time Series Of Counts, Vasiliki Karioti, Chrys Caroni

Journal of Modern Applied Statistical Methods

Models for time series count data include several proposed by Zeger and Qaqish (1988), subsequently generalized into the GARMA family. The GAR(1) model is examined in detail. The maximum likelihood estimation of the parameters will be discussed and the properties of Pearson and randomized residuals will be examined.

Wavelet Techniques In Time Series Analysis With An Application To Space Physics, Agnieszka Jach

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Several wavelet techniques in the analysis of time series are developed and applied to real data sets.

Methods for long-memory models include wavelet-based confidence intervals for the self-similarity parameter in potentially heavy-tailed observations. Empirical coverage probabilities are used to assess the procedures by applying them to Linear Fractional Stable Motion with many choices of parameters. Asymptotic confidence intervals provide empirical coverage often much lower than nominal and it is recommended to use subsampling confidence intervals. A procedure for monitoring the constancy of the self-similarity parameter is proposed and applied to Ethernet data sets.

A test to distinguish a weakly dependent …

Trading The Micro-World Of Combinatorial Complexity For The Macro-World Of Protein Interaction Domains, Nikolay M. Borisov, Nick I. Markevitch, Jan B. Hoek, Boris N. Kholodenko

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

Membrane receptors and proteins involved in signal transduction display numerous binding domains and operate as molecular scaffolds generating a variety of parallel reactions and protein complexes. The resulting combinatorial explosion of the number of feasible chemical species and, hence, different states of a network greatly impedes mechanistic modeling of signaling systems. Here we present novel general principles and identify kinetic requirements that allow us to replace a mechanistic picture of all possible micro-states and transitions by a macro-description of states of separate binding sites of network proteins. This domain-oriented approach dramatically reduces computational models of cellular signaling networks by dissecting …

Extra Exercises On Time Series (Tomas Rau Notes), Maria Carolina Caetano

Econ 240B Section

No abstract provided.

Grinding Wheel Condition Monitoring With Boosted Classifiers, Fengming Tang

LSU Master's Theses

In this thesis, two data sets collected in grinding process under different cutting and wheel conditions were studied. One is the cutting forces in three directions, i.e. X, Y and Z, collected under two different cutting conditions. The other one is the acoustic emission (AE) signals collected under different wheel conditions(sharp and dull). For the goal of grinding wheel condition monitoring, the regression model with autocorrelated errors was proved to be effective and was used to extract features from signals in this study. The coefficients of the models served as the features used in the classification step that employed boosting …