2007 . Bayesian multivariate linear regression with application to changepoint models in hydrometeorological variables . Water Resources Research 43 : W08401 .Asselin, J. J., T. B. M. J. Ouarda, and O. Seidou (2005), Bayesian multi- variate linear regression with application to changepoint ...
For details on the analytically tractable posterior distributions offered by the Bayesian linear regression model framework in Econometrics Toolbox, see Analytically Tractable Posteriors. Otherwise, you must use numerical integration techniques to compute integrals of h(β,σ2) with respect to posterior ...
Stone CJ (1985) Additive regression and other nonparametric models. Ann Stat 13(2):689–705 Article MathSciNet MATH Google Scholar Teuquia ON, Ren J, Planchet F (2014) Internal model in life insurance: application of least squares Monte-Carlo in risk assessment van der vaart AW, Wellner...
4.1 Application to in vitro breast cancer cell line data We first applied the hierarchical Bayesian model to gene expression data measured from in vitro breast cancer cell lines. We chose to use cell line data mainly because such data is usually clean and good for validating computational models...
Bayesian Linear Discriminant Analysis Bayesian logic Bayesian logic Bayesian Management Information Base Bayesian Maximum Entropy Bayesian Model Averaging Bayesian modeling Bayesian Modelling Applications Workshop Bayesian Multivariate Adaptive Regression Spline ...
exploring nonlinear regression methods, with application to association studies. phd thesis, university of cambridge, uk (2010) surjanovic, s., bingham, d.: virtual library of simulation experiments: test functions and datasets. https://www.sfu.ca/ssurjano/index.html (2021) tibshirani, r.: ...
The overall summary is: You can first try linear regression. If this is not appropriate for your problem you can then try pre-transforming your y-data (a log-like or logit transform) and seeing if that fits better. However, if you transform your y-data you are using a new error model...
1 Introduction Although Bayesian methods have been studied for many years, it is only recently that their practical application has become truly widespread. This is due in large part to the relatively high computational overhead of performing the marginalizations (integrations and summa- tions) which...
This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers having a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods. ...
Section 6 uses the empirical relationship between stock returns and dividend yields to illustrate the application of this diagnostic. Section 7 contains some concluding comments. Some of the technical derivations are relegated to appendices. The computer code for the simulation exercise is available as ...