Bayesian Statistical Pragmatism

Discussion of "Statistical Inference: The Big Picture" by R. E. Kass [arXiv:1106.2895]Comment: Published in at http://dx.doi.org/10.1214/11-STS337C the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Comment: Bayesian Checking of the Second Levels of Hierarchical Models

Comment: Bayesian Checking of the Second Levels of Hierarchical Models [arXiv:0802.0743]Comment: Published in at http://dx.doi.org/10.1214/07-STS235A the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Struggles with Survey Weighting and Regression Modeling

The general principles of Bayesian data analysis imply that models for survey responses should be constructed conditional on all variables that affect the probability of inclusion and nonresponse, which are also the variables used in survey weighting and clustering. However, such models can quickly become very complicated, with potentially thousands of poststratification cells. It is then a challenge to develop general families of multilevel probability models that yield reasonable Bayesian inferences. We discuss in the context of several ongoing public health and social surveys. This work is currently open-ended, and we conclude with thoughts on how research could proceed to solve these problems.Comment: This paper commented in: [arXiv:0710.5009], [arXiv:0710.5012], [arXiv:0710.5013], [arXiv:0710.5015], [arXiv:0710.5016]. Rejoinder in [arXiv:0710.5019]. Published in at http://dx.doi.org/10.1214/088342306000000691 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Bayes, Jeffreys, Prior Distributions and the Philosophy of Statistics

Discussion of "Harold Jeffreys's Theory of Probability revisited," by Christian Robert, Nicolas Chopin, and Judith Rousseau, for Statistical Science [arXiv:0804.3173]Comment: Published in at http://dx.doi.org/10.1214/09-STS284D the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Prior distributions for variance parameters in hierarchical models

Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new folded-noncentral- t family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative priors in this family. We use an example to illustrate serious problems with the inverse-gamma family of ``noninformative'' prior distributions. We suggest instead to use a uniform prior on the hierarchical standard deviation, using the half-t family when the number of groups is small and in other settings where a weakly informative prior is desired.Bayesian inference conditional conjugacy folded noncentral-t distribution half-t distribution hierarchical model multilevel model noninformative prior distribution weakly informative prior distribution

Heavy Baryons: A Combined Large N_c and Heavy Quark Expansion for Electroweak Currents

The combined large N_c and heavy quark limit for baryons containing a single heavy quark is discussed. The combined large N_c and heavy quark expansion of the heavy quark bilinear operators is obtained. In the combined expansion the corrections proportional to m_N/m_Q are summed to all orders. In particular, the combined expansion can be used to determine semileptonic form factors of heavy baryons in the combined limit.Comment: 8 pages. Presented at The Phenomenology of Large N_c QCD, Tempe, Arizona, 9-11 Jan 200

Nucleon-Nucleon Scattering and Large N(c) QCD

Nucleon-nucleon scattering observables are discussed in the context of large Nc QCD. As is well known, the baryon spectrum in the large Nc limit exhibits contracted SU(2Nf) spin-flavor sym- metry. This symmetry can be used to derive model-independent relations between proton-proton and proton-neutron total cross sections. These relations are valid in the kinematic regime in which the relative momentum of two nucleons is of order of Nc. In this semiclassical regime the nucleon-nucleon scattering can be described in the time-dependent mean field approximation. These model-independent results are compared to experimental data for spin-independent and polarized total nucleon-nucleon cross sections.Comment: 9 pages, 3 figures. Invited talk, Xth Quark Confinement and the Hadron Spectrum, October 201

Beyond subjective and objective in statistics

We argue that the words "objectivity" and "subjectivity" in statistics discourse are used in a mostly unhelpful way, and we propose to replace each of them with broader collections of attributes, with objectivity replaced by transparency, consensus, impartiality, and correspondence to observable reality, and subjectivity replaced by awareness of multiple perspectives and context dependence. The advantage of these reformulations is that the replacement terms do not oppose each other. Instead of debating over whether a given statistical method is subjective or objective (or normatively debating the relative merits of subjectivity and objectivity in statistical practice), we can recognize desirable attributes such as transparency and acknowledgment of multiple perspectives as complementary goals. We demonstrate the implications of our proposal with recent applied examples from pharmacology, election polling, and socioeconomic stratification.Comment: 35 page

Induction and Deduction in Baysian Data Analysis

The classical or frequentist approach to statistics (in which inference is centered on significance testing), is associated with a philosophy in which science is deductive and follows Popperis doctrine of falsification. In contrast, Bayesian inference is commonly associated with inductive reasoning and the idea that a model can be dethroned by a competing model but can never be directly falsified by a significance test. The purpose of this article is to break these associations, which I think are incorrect and have been detrimental to statistical practice, in that they have steered falsificationists away from the very useful tools of Bayesian inference and have discouraged Bayesians from checking the fit of their models. From my experience using and developing Bayesian methods in social and environmental science, I have found model checking and falsification to be central in the modeling process.philosophy of statistics, decision theory, subjective probability, Bayesianism, falsification, induction, frequentism