scholarly journals Parametric and nonparametric Bayesian model specification: A case study involving models for count data

2008 ◽  
Vol 52 (4) ◽  
pp. 2110-2128 ◽  
Author(s):  
Milovan Krnjajić ◽  
Athanasios Kottas ◽  
David Draper
Keyword(s):  
Count Data ◽  
Bayesian Model ◽  
Model Specification ◽  
Nonparametric Bayesian

Fusion of General Transit Feed Specification (GTFS) and passenger count data: A case study on Cyprus Transit System

2021 ◽  
Author(s):  
Haris Ballis ◽  
Filippos Alogdianakis ◽  
Paraskevas Nikolaou ◽  
Katerina Stylianou ◽  
Loukas Dimitriou
Keyword(s):  
Count Data ◽  
Transit System

scholarly journals TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits

2018 ◽  
Author(s):  
Sini Nagpal ◽  
Xiaoran Meng ◽  
Michael P. Epstein ◽  
Lam C. Tsoi ◽  
Matthew Patrick ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Bayesian Model ◽  
Genetic Architecture ◽  
Bayesian Method ◽  
Association Studies ◽  
Gwas Data ◽  
Nonparametric Bayesian ◽  
Transcriptomic Data ◽  
Special Cases

AbstractThe transcriptome-wide association studies (TWAS) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWAS facilitates gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, we propose an improved Bayesian method that assumes a data-driven nonparametric prior to impute gene expression. Our method is general and flexible and includes both the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan. In real applications, our nonparametric Bayesian method fitted transcriptomic imputation models for 2X number of genes with 1.7X average regression R2 over PrediXcan, thus improving the power of follow-up TWAS. Hence, the nonparametric Bayesian model is preferred for modeling the complex genetic architecture of transcriptomes and is expected to enhance transcriptome-integrated genetic association studies. We implement our Bayesian approach in a convenient software tool “TIGAR” (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWAS using individual-level or summary-level GWAS data.

Writing Bayesian Models

2015 ◽  
Author(s):  
N. Thompson Hobbs ◽  
Mevin B. Hooten
Keyword(s):  
Posterior Distribution ◽  
Bayesian Model ◽  
Joint Distribution ◽  
Structure And Function ◽  
Model Specification ◽  
Sagebrush Steppe ◽  
Building Models ◽  
Steppe Ecosystem ◽  
Writing Models ◽  
And Function

This chapter offers a general set of steps for writing models to assist the researcher in formulating their own approach to the Bayesian model. The crucial skill of specifying models is often neglected in statistical texts in general and texts on Bayesian modeling in particular. The central importance of model specification also motivates this chapter. The overarching challenge in building models is to specify the components of the posterior distribution and the joint distribution and to factor the joint distribution into sensible parts. This chapter first lays out a framework for doing just that, albeit in somewhat abstract terms, before moving on to a more concrete example—the effects of grazing by livestock and wild ungulates on structure and function of a sagebrush steppe ecosystem.

Problems with Avian Point Counts for Estimating Density of Northern Bobwhite—A Case Study

2011 ◽  
Vol 2 (1) ◽  
pp. 117-121 ◽  
Author(s):  
Roger D. Applegate ◽  
Robert E. Kissell ◽  
E. Daniel Moss ◽  
Edward L. Warr ◽  
Michael L. Kennedy
Keyword(s):  
Count Data ◽  
Distance Sampling ◽  
Bird Species ◽  
Northern Bobwhite ◽  
Model Fit ◽  
Point Count ◽  
Point Counts ◽  
Occupancy Estimation ◽  
Detection Probabilities

Abstract Point count data are used increasingly to provide density estimates of bird species. A favored approach to analyze point count data uses distance sampling theory where model selection and model fit are important considerations. We used uniform and half normal models and assessed model fit using χ2 analysis. We were unsuccessful in fitting models to 635 northern bobwhite Colinus virginianus observations from 85 avian point locations spanning 6 y (P ≤ 0.05). Most observations (74%) occurred in the outermost (>100-m) distance radius. Our results violated the assumptions that all observations at the point are detected. The assumption that birds were assigned to the correct distance interval also was probably violated. We caution managers in implementing avian point counts with distance sampling when estimating northern bobwhite population density. We recommend exploring other approaches such as occupancy-estimation and modeling for estimating detection probabilities.

Optimal Nonparametric Bayesian Model-Based Multimodal BoVW Creation Using Multilayer pLSA

2019 ◽  
Vol 39 (2) ◽  
pp. 1123-1132 ◽  
Author(s):  
G. Nagarajan ◽  
R. I. Minu ◽  
A. Jayanthila Devi
Keyword(s):  
Bayesian Model ◽  
Nonparametric Bayesian ◽  
Model Based

scholarly journals About Statistical Analysis of Qualitative Survey Data

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Stefan Loehnert
Keyword(s):  
Data Aggregation ◽  
Qualitative Data ◽  
Correlation Coefficients ◽  
Model Specification ◽  
Data Sets ◽  
Statistical Methodology ◽  
Qualitative Survey ◽  
Reliability Check ◽  
Scale Transformations

Gathered data is frequently not in a numerical form allowing immediate appliance of the quantitative mathematical-statistical methods. In this paper are some basic aspects examining how quantitative-based statistical methodology can be utilized in the analysis of qualitative data sets. The transformation of qualitative data into numeric values is considered as the entrance point to quantitative analysis. Concurrently related publications and impacts of scale transformations are discussed. Subsequently, it is shown how correlation coefficients are usable in conjunction with data aggregation constrains to construct relationship modelling matrices. For illustration, a case study is referenced at which ordinal type ordered qualitative survey answers are allocated to process defining procedures as aggregation levels. Finally options about measuring the adherence of the gathered empirical data to such kind of derived aggregation models are introduced and a statistically based reliability check approach to evaluate the reliability of the chosen model specification is outlined.

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