A Bayesian nonparametric approach to modeling market share dynamics

Every year, the U.S. government provides several billions of dollars in the form of federal funding for transportation services in the U.S.A. Decision making with regard to the use of these funds largely depends on performance indicators like average annual daily traffic (AADT). In this paper, Bayesian nonparametric models are developed through machine learning for the estimation of AADT on bridges. The effect of hyperparameter choice on the accuracy of estimations produced by Bayesian nonparametric models is also assessed. The predictions produced using the Bayesian nonparametric approach are then compared with predictions from a popular Frequentist approach for the selected bridges. Evaluation metrics like the mean absolute percentage error are subsequently employed in model evaluation. Based on the results, the best methods for AADT forecasting for the selected bridges are recommended.

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Do School Districts Affect NYC House Prices? Identifying Border Differences Using a Bayesian Nonparametric Approach to Geographic Regression Discontinuity Designs

Journal of the American Statistical Association ◽

10.1080/01621459.2020.1817749 ◽

2020 ◽

pp. 1-13

Author(s):

Maxime Rischard ◽

Zach Branson ◽

Luke Miratrix ◽

Luke Bornn

Keyword(s):

School Districts ◽

Regression Discontinuity ◽

House Prices ◽

Bayesian Nonparametric ◽

Nonparametric Approach ◽

Regression Discontinuity Designs

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A Bayesian Nonparametric Approach to Discover Clinico-Genetic Associations across Cancer Types

10.1101/623215 ◽

2019 ◽

Author(s):

Melanie F. Pradier ◽

Stephanie L. Hyland ◽

Stefan G. Stark ◽

Kjong Lehmann ◽

Julia E. Vogt ◽

...

Keyword(s):

Cancer Biology ◽

Multiple Hypothesis Testing ◽

Environmental Data ◽

Bayesian Nonparametric ◽

Genetic Associations ◽

Disease Treatment ◽

Nonparametric Approach ◽

Gene Level ◽

Cancer Types ◽

Unsupervised Approach

AbstractMotivationPersonalized medicine aims at combining genetic, clinical, and environmental data to improve medical diagnosis and disease treatment, tailored to each patient. This paper presents a Bayesian nonparametric (BNP) approach to identify genetic associations with clinical/environmental features in cancer. We propose an unsupervised approach to generate data-driven hypotheses and bring potentially novel insights about cancer biology. Our model combines somatic mutation information at gene-level with features extracted from the Electronic Health Record. We propose a hierarchical approach, the hierarchical Poisson factor analysis (H-PFA) model, to share information across patients having different types of cancer. To discover statistically significant associations, we combine Bayesian modeling with bootstrapping techniques and correct for multiple hypothesis testing.ResultsUsing our approach, we empirically demonstrate that we can recover well-known associations in cancer literature. We compare the results of H-PFA with two other classical methods in the field: case-control (CC) setups, and linear mixed models (LMMs).

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