4,781 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
BAYESIAN ANALYSIS OF TOBIT QUANTILE REGRESSION WITH ADAPTIVE LASSO PENALTY IN HOUSEHOLD EXPENDITURE FOR CIGARETTE CONSUMPTION
Tobit Quantile Regression with Adaptive Lasso Penalty is a quantile regression model on censored data that adds Lasso's adaptive penalty to its parameter estimation. The estimation of the regression parameters is solved by Bayesian analysis. Parameters are assumed to follow a certain distribution called the prior distribution. Using the sample information along with the prior distribution, the conditional posterior distribution is searched using the Box-Tiao rule. Computational solutions are solved by the MCMC Gibbs Sampling algorithm. Gibbs Sampling can generate samples based on the conditional posterior distribution of each parameter in order to obtain a posterior joint distribution. Tobit Quantile Regression with Adaptive Lasso Penalty was applied to data on Household Expenditure for Cigarette Consumption in 2011. As a comparison for data analysis, Tobit Quantile Regression was used. The results of data analysis show that the Tobit Quantile Regression model with Adaptive Lasso Penalty is better than the Tobit Quantile Regression
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas
This paper provides a comprehensive tutorial for Bayesian practitioners in
pharmacometrics using Pumas workflows. We start by giving a brief motivation of
Bayesian inference for pharmacometrics highlighting limitations in existing
software that Pumas addresses. We then follow by a description of all the steps
of a standard Bayesian workflow for pharmacometrics using code snippets and
examples. This includes: model definition, prior selection, sampling from the
posterior, prior and posterior simulations and predictions, counter-factual
simulations and predictions, convergence diagnostics, visual predictive checks,
and finally model comparison with cross-validation. Finally, the background and
intuition behind many advanced concepts in Bayesian statistics are explained in
simple language. This includes many important ideas and precautions that users
need to keep in mind when performing Bayesian analysis. Many of the algorithms,
codes, and ideas presented in this paper are highly applicable to clinical
research and statistical learning at large but we chose to focus our
discussions on pharmacometrics in this paper to have a narrower scope in mind
and given the nature of Pumas as a software primarily for pharmacometricians
Attribute network models, stochastic approximation, and network sampling and ranking algorithms
We analyze dynamic random network models where younger vertices connect to
older ones with probabilities proportional to their degrees as well as a
propensity kernel governed by their attribute types. Using stochastic
approximation techniques we show that, in the large network limit, such
networks converge in the local weak sense to randomly stopped multitype
branching processes whose explicit description allows for the derivation of
asymptotics for a wide class of network functionals. These asymptotics imply
that while degree distribution tail exponents depend on the attribute type
(already derived by Jordan (2013)), Page-rank centrality scores have the
\emph{same} tail exponent across attributes. Moreover, the mean behavior of the
limiting Page-rank score distribution can be explicitly described and shown to
depend on the attribute type. The limit results also give explicit formulae for
the performance of various network sampling mechanisms. One surprising
consequence is the efficacy of Page-rank and walk based network sampling
schemes for directed networks in the setting of rare minorities. The results
also allow one to evaluate the impact of various proposed mechanisms to
increase degree centrality of minority attributes in the network, and to
quantify the bias in inferring about the network from an observed sample.
Further, we formalize the notion of resolvability of such models where, owing
to propagation of chaos type phenomenon in the evolution dynamics for such
models, one can set up a correspondence to models driven by continuous time
branching process dynamics.Comment: 48 page
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