Caitlin E. Dipak D. Yasuko Chikuse. Hannu Oja. Radford M. Adrian Baddeley. Peter Spirtes. Sneh Gulati.
BETASIMU: Community data simulation from beta response function
Jane F. Fritz L. Peter Donnelly. Shun-Ichi Amari. Home Contact us Help Free delivery worldwide. Free delivery worldwide. Bestselling Series. Harry Potter. Popular Features. New Releases. Description The aim of this book is to present a survey of the many ways in which the statistical package GLIM may be used to model and analyze stochastic processes. Its emphasis is on using GLIM interactively to apply statistical techniques, and examples are drawn from a wide range of applications including medicine, biology, and the social sciences.
It is based on the author's many years of teaching courses along these lines to both undergraduate and graduate students. The author assumes that readers have a reasonably strong background in statistics such as might be gained from undergraduate courses and that they are also familiar with the basic workings of GLIM.
The Karl Pearson Prize for Contemporary Research Contribution
Topics covered include: the analysis of survival data, regression and fitting distributions, time series analysis including both the time and frequency domains , repeated measurements, and generalized linear models. Product details Format Paperback pages Dimensions x x Other books in this series. Add to basket. Ranked Set Sampling Zehua Chen.
Simulation with beta response function
Tools for Constructing Chronologies Caitlin E. Statistics on Special Manifolds Yasuko Chikuse. Causation, Prediction, and Search Peter Spirtes. Nonlinear Estimation and Classification D.
Table of contents 1. Normal Theory Models and Some Extensions. Linear Regression. This is a course on the use of wavelets methods in statistics. The course introduces wavelet theory, provides an overview of wavelet-based statistical methods. Topics include smoothing of noisy signals, estimation function data and representation of stochastic processes. Some emphasis is given to Bayesian procedures.
Prerequisite: STAT or approval by the instructor. Time Series Analysis I. An introduction to diverse modes of analysis now available to solve for univariate time series; basic problems of parameter estimation, spectral analysis, forecasting and model identification. Time Series Analysis II. Multiple time series, ARMA models, test of hypotheses, estimation of spectral density matrix, transfer function and forecasting. Credit 1. Oral presentations of special topics and current research in statistics.
May be repeated for credit. Prerequisite: Graduate classification in statistics. Professional Internship. Credit 1 to 3.
Practicum in statistical consulting for students in Ph. Students will be assigned consulting problems brought to the Department of Statistics by researchers in other disciplines. Directed Studies. Credit 1 to 6. Individual instruction in selected fields in statistics; investigation of special topics not within scope of thesis research and not covered by other formal courses. Prerequisites: Graduate classification and approval of department head.
Special Topics in Statistics.
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Credit 1 to 4. Selected topics in an identified area of statistics. Open to non-majors. Credit 1 or more. Research for thesis or dissertation. Prerequisite: Graduate classification. Analyzing such data and obtaining proper concordance among the instruments is challenging when the physical source models are not well understood, when there are uncertainties in "known" physical quantities, or when data quality varies in ways that cannot be fully quantified.
Furthermore, the number of model parameters increases with both the number of instruments and the number of sources. Thus, concordance of the instruments requires careful modeling of the mean signals, the intrinsic source differences, and measurement errors. We propose a log-Normal model and a more general log-t model that respect the multiplicative nature of the mean signals via a half-variance adjustment, yet permit imperfections in the mean modeling to be absorbed by residual variances.
We present analytical solutions in the form of power shrinkage in special cases and develop reliable Markov chain Monte Carlo algorithms for general cases. The priors play an important role in the identifiability of our model thus properly quantifying the influence from the prior is very important.
In this talk, our ongoing work on quantifying prior influence will be briefly discussed under the instrument calibration context. We apply our method to several datasets including a combination of observations of active galactic nuclei AGN and spectral line emission from the supernova remnant E, obtained with a variety of X-ray telescopes such as Chandra, XMM-Newton, Suzaku, and Swift. We demonstrate that our method provides helpful and practical guidance for astrophysicists when adjusting for disagreements among instruments.
Friday, October 4, , a. BLOC First, we propose a calibration weighting estimator that uses only covariate information from the RWE study. Because this estimator enforces the covariate balance between the RCT and RWE study, the generalizability of the trial-based estimator is improved.
We further propose a doubly robust augmented calibration weighting estimator that can be applied in the event that treatment and outcome information is also available from the RWE study. This estimator achieves the semiparametric efficiency bound derived under the identification and outcome mean function transportability assumptions when the nuisance models are correctly specified. A data-adaptive nonparametric sieve method is provided as an alternative to the parametric approach.
The sieve method guarantees good approximation of the nuisance models. We establish asymptotic results under mild regularity conditions and confirm the finite sample performances of the proposed estimators by simulation experiments.
We apply our proposed methods to estimate the effect of adjuvant chemotherapy in early-stage resected non-small-cell lung cancer integrating data from a RCT and a sample from the National Cancer Database. Friday, October 18, , a. Rss Email Tweet facebook. Calendar Maps Directory Contact Us.
Graduate Courses. Abstract: This course covers joint modeling of parameters in the biparametric exponential family of distributions, under classic and Bayesian perspectives, including generalized linear models, heteroscedastic linear and nonlinear regression models and joint modeling of the mean and precision the variance with application to gamma and beta regression models. The classic Fisher scoring algorithm is introduced to fit the proposed models, including an iterative Fisher scoring algorithm to estimate mean and shape parameters in gamma regression models, taking into account the orthogonality of these parameters.
A Bayesian estimation method with corresponding results of simulated studies and applications is also presented. In addition, the course highlights some extensions including double generalized random effects models, overdispersed regression models and mixture regression models in the biparametric exponential family.
M3: Modeling Ordinal Categorical Data. Abstract: This mini-course surveys methods for analyzing categorical response variables that have a natural ordering of the categories. Such data often occur in the social sciences e.