Bayesian Hypothesis Testing for Hospitality and Tourism Research

2020 ◽  
pp. 109634802094732
Author(s):  
A. George Assaf ◽  
Mike Tsionas
Keyword(s):  
Hypothesis Testing ◽  
Bayesian Approach ◽  
Prior Distributions ◽  
P Values ◽  
Tourism Research ◽  
Bayesian Hypothesis Testing ◽  
Main Challenge ◽  
Hospitality And Tourism ◽  
Practical Recommendations ◽  
The Bayesian Approach

In hospitality and tourism research, p-values continue to be the most common approach to hypothesis testing. In this article, we elaborate on some of the misconceptions associated with p-values. We discuss the advantages of the Bayesian approach and provide several important practical recommendations and considerations for Bayesian hypothesis testing. With the main challenge of Bayesian hypothesis testing being the sensitivity of the results to prior distributions, we present in this article several priors that can be used for that purpose and illustrate their performance in a regression context.

scholarly journals On Two Measure-Theoretic Aspects of the Full Bayesian Significance Test for Precise Bayesian Hypothesis Testing †

2021 ◽  
Vol 3 (1) ◽  
pp. 10
Author(s):  
Riko Kelter
Keyword(s):  
Hypothesis Testing ◽  
Convenient Method ◽  
Prior Probability ◽  
Significance Test ◽  
Statistical Evidence ◽  
Posterior Density ◽  
Absolutely Continuous ◽  
Prior Distributions ◽  
P Values ◽  
Bayesian Hypothesis Testing

The Full Bayesian Significance Test (FBST) has been proposed as a convenient method to replace frequentist p-values for testing a precise hypothesis. Although the FBST enjoys various appealing properties, the purpose of this paper is to investigate two aspects of the FBST which are sometimes observed as measure-theoretic inconsistencies of the procedure and have not been discussed rigorously in the literature. First, the FBST uses the posterior density as a reference for judging the Bayesian statistical evidence against a precise hypothesis. However, under absolutely continuous prior distributions, the posterior density is defined only up to Lebesgue null sets which renders the reference criterion arbitrary. Second, the FBST statistical evidence seems to have no valid prior probability. It is shown that the former aspect can be circumvented by fixing a version of the posterior density before using the FBST, and the latter aspect is based on its measure-theoretic premises. An illustrative example demonstrates the two aspects and their solution. Together, the results in this paper show that both of the two aspects which are sometimes observed as measure-theoretic inconsistencies of the FBST are not tenable. The FBST thus provides a measure-theoretically coherent Bayesian alternative for testing a precise hypothesis.

scholarly journals Updating Knowledge in The Estimation of The Genetics Parameters Multi-trait and Multi-Environment Bayesian Analysis in Rice (Oryza Sativa L.)

2021 ◽  
Author(s):  
Camila Ferreira Azevedo ◽  
Cynthia Barreto ◽  
Matheus Suela ◽  
Moysés Nascimento ◽  
Antônio Carlos Júnior ◽  
...  
Keyword(s):  
Bayesian Approach ◽  
Prior Distribution ◽  
Variance Components ◽  
Genetic Correlations ◽  
Bayesian Framework ◽  
Prior Distributions ◽  
Informative Prior ◽  
Informative Prior Distribution ◽  
Informative Prior Distributions ◽  
The Bayesian Approach

Abstract Among the multi-trait models used to jointly study several traits and environments, the Bayesian framework has been a preferable tool for using a more complex and biologically realistic model. In most cases, the non-informative prior distributions are adopted in studies using the Bayesian approach. Still, the Bayesian approach tends to present more accurate estimates when it uses informative prior distributions. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models under a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data from rice. The study involved data pertained to rice genotypes in three environments and five agricultural years (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components and genetic and non-genetic parameters were estimated by the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. The use of more informative prior distributions makes it possible to detect genetic correlations between traits, which cannot be achieved with the use of non-informative prior distributions. Therefore, this mechanism presented for updating knowledge to the elicitation of an informative prior distribution can be efficiently applied in rice genetic selection.

The Re-Evaluation of Structural Reliability based on Identification Results

1999 ◽  
Vol 15 (3) ◽  
pp. 109-116
Author(s):  
Pei-Ling Liu ◽  
Yi-Song Chen
Keyword(s):  
Sensitivity Analysis ◽  
System Identification ◽  
Reliability Analysis ◽  
Bayesian Approach ◽  
Structural Reliability ◽  
Service System ◽  
Maintenance Strategy ◽  
Prior Distributions ◽  
The Bayesian Approach

AbstractThis paper develops a method to re-evaluate the reliability of a structure after a period of service. System identification is performed on the structure to identify the current properties of the structure. The Bayesian approach is adopted to modify the prior distributions of the properties based on the identification results. Then, reliability analysis is performed on the structure using the updated distributions of the properties. Sensitivity analysis is also performed to attain the maintenance strategy.

scholarly journals Method to Obtain a Vector of Hyperparameters: Application in Bernoulli Trials

2020 ◽  
Vol 43 (2) ◽  
pp. 183-209
Author(s):  
Llerzy Esneider Torres Ome ◽  
Jose Rafael Tovar Cuevas
Keyword(s):  
Probability Distribution ◽  
Bayesian Approach ◽  
Prior Distribution ◽  
Sufficient Conditions ◽  
Indigenous Community ◽  
Bernoulli Trials ◽  
Prior Distributions ◽  
The Bayesian Approach

The main difficulties when using the Bayesian approach are obtaining information from the specialist and obtaining hyperparameters values of the assumed probability distribution as representative of knowledge  external to the  data. In addition to the  fact  that  a large  part  of the  literature on this subject is characterized by considering prior conjugated distributions for the parameter of interest. An method is proposed  to find the hyperparameters of a nonconjugated prior  distribution. The following  scenarios were considered for Bernoulli trials: four prior distributions (Beta, Kumaraswamy, Truncated Gamma   and   Truncated  Weibull) and four scenarios  for  the  generating process. Two necessary,  but not sufficient  conditions were  identified to ensure   the  existence of  a  vector of  values for  the  hyperparameter. The Truncated Weibull prior distribution performed the worst.  The methodology was  used  to estimate the  prevalence of two  transmitted sexually infections in an Colombian  indigenous community.

scholarly journals Advantages Masquerading as ‘Issues’ in Bayesian Hypothesis Testing: A Commentary on Tendeiro and Kiers (2019)

2019 ◽  
Author(s):  
Don van Ravenzwaaij ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Bayes Factor ◽  
Statistical Evidence ◽  
Central Component ◽  
P Values ◽  
Bayesian Hypothesis Testing ◽  
Bayesian Testing

Tendeiro and Kiers (2019) provide a detailed and scholarly critique of Null Hypothesis Bayesian Testing (NHBT) and its central component –the Bayes factor– that allows researchers to update knowledge and quantify statistical evidence. Tendeiro and Kiers conclude that NHBT constitutes an improvement over frequentist p-values, but primarily elaborate on a list of eleven ‘issues’ of NHBT. In this commentary, we provide context to each issue and conclude that many issues may in fact be conceived as pronounced advantages of NHBT.

scholarly journals A new Bayesian RAIM for Multiple Faults Detection and Exclusion in GNSS

2014 ◽  
Vol 68 (3) ◽  
pp. 465-479 ◽  
Author(s):  
Qianqian Zhang ◽  
Qingming Gui
Keyword(s):  
Fault Detection ◽  
Hypothesis Testing ◽  
Bayesian Approach ◽  
Gibbs Sampler ◽  
Prior Distribution ◽  
Satellite Observation ◽  
Multiple Faults ◽  
Bayesian Hypothesis Testing ◽  
Receiver Autonomous Integrity Monitoring ◽  
Classification Variable

A new Bayesian approach for multiple satellite faults detection and exclusion is proposed by introducing a classification variable to each satellite observation. If we treat this classification variable as random and assume a prior distribution for it, then a rule for satellite fault detection and exclusion based on the posterior probabilities of the classification variables is constructed under the framework of Bayesian hypothesis testing. Secondly, the Gibbs sampler is introduced to compute the posterior probabilities of the classification variables. Then the implementation for a Bayesian Receiver Autonomous Integrity Monitoring (RAIM) algorithm is designed with the Gibbs sampler. Finally, different schemes are designed to evaluate the performance of the new Bayesian RAIM algorithm in the case of multiple faults. We compare the method in this paper with the Range Consensus (RANCO) method. Experiments illustrate that the proposed algorithm in this paper is capable of detecting and eliminating multiple satellite faults, and the probability of correctly detecting faults is high.

Bayesian statistics

2010 ◽  
pp. 477-497
Author(s):  
Janet L. Peacock ◽  
Philip J. Peacock
Keyword(s):  
Bayesian Statistics ◽  
Bayesian Approach ◽  
Bayesian Methods ◽  
Posterior Distributions ◽  
Bayesian Analyses ◽  
Prior Distributions ◽  
Unknown Quantity ◽  
Frequentist Approach ◽  
Pros And Cons ◽  
The Bayesian Approach

Bayesian statistics 478 How Bayesian methods work 480 Prior distributions 482 Likelihood; posterior distributions 484 Summarizing and presenting results 486 Using Bayesian analyses in medicine 488 Software for Bayesian statistics 492 Reading Bayesian analyses in papers 494 Bayesian methods: a summary 496 In this chapter we describe the Bayesian approach to statistical analysis in contrast to the frequentist approach. We describe how Bayesian methods work including a description of prior and posterior distributions. We outline the role and choice of prior distributions and how they are combined with the data collected to provide an updated estimate of the unknown quantity being studied. We include examples of the use of Bayesian methods in medicine, and discuss the pros and cons of the Bayesian approach compared with the frequentist approach Finally, we give guidance on how to read and interpret Bayesian analyses in the medical literature....

scholarly journals Correcting for endogeneity in hospitality and tourism research

2020 ◽  
Vol 32 (8) ◽  
pp. 2657-2675 ◽  
Author(s):  
A. George Assaf ◽  
Mike Tsionas
Keyword(s):  
Instrumental Variables ◽  
Design Methodology ◽  
Future Research ◽  
Regression Estimation ◽  
Tourism Research ◽  
Content Type ◽  
The Common ◽  
Hospitality And Tourism ◽  
Practical Recommendations

Purpose This paper aims to foster a new discussion on endogeneity in hospitality and tourism research. Design/methodology/approach This paper elaborates on some of the common sources of endogeneity and the methods available to address them. Findings The authors present a variety of methods that can be used to mitigate the endogeneity problem. The authors provide simulation evidence regarding the risk of incorrectly selecting instrumental variables. The authors also provide several important practical recommendations for future research. Research limitations/implications There are other issues and methods of correcting for endogeneity, that is not covered in this paper. However, the paper focuses on issues and methods that can be generalized to most contexts. Originality/value The paper provides practical recommendations for more rigorous regression estimation.

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