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In Bayesian Model selection the quantities of interest are marginal distributions and Bayes Factors Though these have been shown to behave well when prior distributions are proper and have nice coherency properties they also give rise to the Jeffreys Lindley paradox when improper priors are used We will look at several methods that have been developed for use with improper priors discuss those that can provide consistency and derive some

Original URL path: http://www.stat.ufl.edu/info/seminar-abstracts/2011.09.08.awomack.abstract.txt (2015-11-09)

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background and presence only samples are selected without bias and provided the covariates of species occurrence and species detection probabilities are distinct and independently distributed Unlike other species distribution models e g Maxent this approach does not require species prevalence over the study area to be known Simulations were used to illustrate the operating characteristics of the estimator under different sample sizes and to make comparisons with site occupancy estimates

Original URL path: http://www.stat.ufl.edu/info/seminar-abstracts/2011.09.15.rdorazio.abstract.txt (2015-11-09)

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covariates These estimators have the appealing property that they are consistent for the true population mean even if one of the outcome regression or propensity score models but not both is misspecified However despite this appealing property the usual doubly robust estimator may yield severely biased inferences if neither of these models is correctly specified and can exhibit nonnegligible bias if the estimated propensity score is close to zero for

Original URL path: http://www.stat.ufl.edu/info/seminar-abstracts/2011.09.29.mdavidian.abstract.txt (2015-11-09)

Open archived version from archive- Hani Doss's Home Page

I was at Ohio State from 1994 to 2005 and before that I was at Florida State for 12 years For more information about me see my CV in PostScript or PDF format Contact information Department of Statistics University of

Original URL path: http://www.stat.ufl.edu/~doss/ (2015-11-09)

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is stated as follows Suppose we fix a function f of theta How do we efficiently estimate the posterior expectation of f theta simultaneously for all hyperparameter values The second problem is how do we identify subsets of the hyperparameter space H which give rise to reasonable choices of h We assume that we are able to generate Markov chain samples from the posterior for a finite number of the

Original URL path: http://www.stat.ufl.edu/info/seminar-abstracts/2011.10.06.hdoss.abstract.txt (2015-11-09)

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model allowing transformation and dependence Typically this problem is solved by MCMC sampling from the posterior density pi of beta given Y However since each evaluation of pi requires an expensive run of f naive sampling of pi by MCMC to obtain a nontrivial effective sample size is computationally prohibitive To reduce computational burden we limit evaluation of f to a small number of points chosen on a high probability region of pi reached by optimization Then we approximate the logarithm of pi using radial basis functions and use the resulting cheap to evaluate surface in MCMC The main challenge is to determine the approximation region properly The methodology is subsequently extended to statistical models in which it is possible to identify a minimal subvector beta of the whole parameter vector eta responsible for the expensive computation in the evaluation of pi We propose two approaches DOSKA and INDA that approximate pi by interpolation in ways that exploit this computational structure to mitigate the curse of dimensionality DOSKA interpolates pi directly while INDA interpolates pi indirectly by interpolating functions e g a regression function upon which pi depends Our primary contribution is derivation of a GP interpolant that provably improves

Original URL path: http://www.stat.ufl.edu/info/seminar-abstracts/2011.10.20.nbliznyuk.abstract.txt (2015-11-09)

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components a subject specific functional random intercept that quantifies the cross sectional variability a subject specific functional slope that quantifies the dynamic irreversible deformation over multiple visits and a subject visit specific functional deviation that quantifies exchangeable or reversible visit to visit changes The proposed method is very fast scalable to studies including ultra high dimensional data and can easily be adapted to and executed on modest computing infrastructures The

Original URL path: http://www.stat.ufl.edu/info/seminar-abstracts/2011.10.27.vzipunnikov.abstract.txt (2015-11-09)

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the great innovator who from the 1850s developed the formulation and standardisation of distributions the common use of the Normal distribution probability plotting regression correlation and the use of ordered data However more mathematical statisticians such a Karl Pearson who wrote the great biography of Galton redefined this formulation of statistics to provide a more manageable mathematical statistics and this became the statistics we were taught as students However if

Original URL path: http://www.stat.ufl.edu/info/seminar-abstracts/2011.11.03.wgilchrist.abstract.txt (2015-11-09)

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