Dissertation Modeling Series Time

Dissertation Modeling Series Time-85
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The LRTM uses a parametric latent curve that allows customers to exhibit a broad range of latent patterns of evolution while providing structure to allow for robust estimation.

The latent trajectory then maps onto behaviors akin to a dynamic factor model that allows multiple behaviors to be jointly related over time.

The research of the Rice Statistics Department has applications in medicine, finance, energy, and the environment.

Our faculty and students have made contributions to statistical theory and methods including modeling of contagious diseases, the genetics of cancer, neuroimaging, machine learning, applied probability, and many other areas.

Le PDF Spatially Random Processes in One-Dimensional Maps: the Logistic Map and the Arnold Circle Map, An Tuong Le PDF Toward a General Solution of the Three-Wave Resonant Interaction Equations, Ruth Ann Martin PDF Rays, Waves, and Separatrices, Cristian Rafael Mendoza PDF From Nonlinear Embedding to Graph Distances: A Spectral Perspective, Nathan D.

Dissertation Modeling Series Time

Leibs PDF Fundamental Limits of Network Communication with General Message Sets: A Combinatorial Approach, Henry Paul Romero PDF The Effectiveness of Calculus Workgroup on Student Performance in Calculus: A Mixed-Methods Approach, Brita B.

Modeling how customers evolve over time has received attention among researchers and practitioners due to the increasing importance of individual-level prediction in customer data sets.

A class of time-series models known as the Hidden Markov Model (HMM) has emerged in recent years as the empirical workhorse in capturing such non-stationary customer behaviors over time in the marketing literature.

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Ice-Sheet Models Using a Fluidity-Based FOSLS Approach to Nonlinear-Stokes Flow., Jeffery M.

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