Discussion of “Nonparametric Bayesian Inference in Applications”: Bayesian nonparametric methods in econometrics

Griffin, Jim E.; Kalli, Maria; Steel, Mark F. J.

doi:10.1007/s10260-017-0384-0

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…When the posterior estimate cannot be determined analytically, it can be drawn from computational sampling techniques such as Markov Chain Monte Carlo (MCMC) methods (Herath, 2019). MCMC is often used to derive a probability estimation of a density given limited information about the distribution (Martino et al, 2018;Gu et al, 2019;Griffin et al, 2018). This development of MCMC methods has been made possible as a result of technological advancements and relevant up to date software (Herath, 2019).…”

Section: Bayesian Approach 31 Backgroundmentioning

confidence: 99%

A solution to the risk-return conundrum: Return exposure and the nonparametric Bayesian approach

Dwarika

2023

IJMRGE

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The topic of the risk-return relationship is of broad importance in the fields of finance and economics. It has been widely investigated on an international scale, especially by developed markets from as early as the 1950's, with the primary motive being to help market participants optimize their chance to earn higher returns. According to conventional economic theory, the relationship between risk and return is a positive and linear relationship – the higher the risk, the higher the return. However, there are many studies documented in literature which show a positive or negative or no relationship at all. As a result, due to the magnitude of conflicting results over the years, this has caused an ongoing debate to arise regarding the risk-return relationship. International studies have explored a number of theories and models to attempt resolving the inconclusive empirical backing of the risk-return relationship. A valuable contribution of this study is the introduction of the novel concept “returns exposure” which refers to the risk that arises from the asymmetric nature of returns. This measure has a certain level of uncertainty attached to it due to its latent and stochastic nature. As a result, it may be ineffectively accounted for by existing parametric methods such as regression analysis and GARCH type models which are prone to model misspecification. This motivates the use of a more robust method, namely, the nonparametric Bayesian approach. The Bayesian approach has the ability to average out sources of uncertainty and measurement errors and thus effectively account for “return risk” or “returns exposure.” The Bayesian approach can be modelled within a parametric or nonparametric framework. The nonparametric approach is considered more robust as it relaxes modelling assumptions such as normality. Thus, in combination with the nonparametric approach, this provides a more robust estimation of the risk-return relationship.

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Section: Bayesian Approach 31 Backgroundmentioning

confidence: 99%

A solution to the risk-return conundrum: Return exposure and the nonparametric Bayesian approach

Dwarika

2023

IJMRGE

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“…Appropriateness of analyses also refers to the distribution of variables. Whereas in the last decade, Bayesian parametric tests have often been used and discussed, Bayesian non-parametric tests have been utilized less often (for an exception see Yuan and Johnson, 2008 ; Ghosal and Vaart, 2017 ; Griffin et al, 2017 ; Hai, 2017 ). This is important, however, because some variables are known to be not normally distributed.…”

Section: Introductionmentioning

confidence: 99%

The Role of Perceived Energy and Self-Beliefs for Physical Activity and Sports Activity of Patients With Multiple Sclerosis and Chronic Stroke

et al. 2021

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Physical activity counteracts some of the negative consequences associated with chronic neurological diseases. Here, we describe the levels of physical activity (PA) and sports activity (Sport) in patients with multiple sclerosis (pMS, n = 59) and chronic stroke (pStroke, n = 67) and test compliance with the recommendation for health-promoting physical activity of the World-Health Organization (WHO). Secondly, we tested for differences between the groups of patients, and thirdly, we examined relationships between PA and Sport with psychological indicators of perceived energy (fatigue and vitality) and self-beliefs (self-efficacy and self-control). Psychological constructs were assessed with validated measures from different disciplines in Psychology. A statistical aim was to describe interpretations gained by (non-) parametric Bayesian and Null-Hypothesis-Significance Testing statistics (NHST) on the example of the conducted tests for differences and relationships. Descriptive analyses revealed that pMS and pStroke complied with recommendations of the WHO, but with large variance indicating that patient groups are not homogenous. Tests for differences showed that the PA difference between pMS and pStroke can be attributed to the higher proportion of women in the pMS sample as they engage more in household chores (important part of PA). Tests for relationships showed that for pStroke, vitality, self-control, and self-efficacy were positively related to the level of sports activity. Furthermore, pStroke who were sport active had lower fatigue and higher self-control and self-efficacy scores than sport inactive people. Although they address slightly different questions, the Bayesian and the NHST approach led to similar general conclusions.

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“…It is one of the most important and complex types of Probability Graph models. Therefore, the inference of Non-parametric Bayesian model has always been an important research direction of probability model [Griffin, Kalli and Steel (2018)], such as variational inference [Yao, Vehtari, Simpson et al (2018)] and regression analysis [Seo, Wallat, Graepel et al (2000)]. The Beta Process is a Non-parametric Bayesian model.…”

Section: Introductionmentioning

confidence: 99%

The Exact Inference of Beta Process and Beta Bernoulli Process From Finite Observations

Cheng¹,

Li²,

Jiang³

2019

Computer Modeling in Engineering &Amp; Sciences

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Beta Process is a typical nonparametric Bayesian model. and the Beta Bernoulli Process provides a Bayesian nonparametric prior for models involving collections of binary valued features. Some previous studies considered the Beta Process inference problem by giving the Stick-Breaking sampling method. This paper focuses on analyzing the form of precise probability distribution based on a Stick-Breaking approach, that is, the joint probability distribution is derived from any finite number of observable samples: It not only determines the probability distribution function of the Beta Process with finite observation (represented as a group of number between [ ] 0,1), but also gives the distribution function of the Beta Bernoulli Process with the same finite dimension (represented as a matrix with element value of 0 or, 1) by using this distribution as a prior.

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Discussion of “Nonparametric Bayesian Inference in Applications”: Bayesian nonparametric methods in econometrics

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 5 publications

References 43 publications

A solution to the risk-return conundrum: Return exposure and the nonparametric Bayesian approach

A solution to the risk-return conundrum: Return exposure and the nonparametric Bayesian approach

The Role of Perceived Energy and Self-Beliefs for Physical Activity and Sports Activity of Patients With Multiple Sclerosis and Chronic Stroke

The Exact Inference of Beta Process and Beta Bernoulli Process From Finite Observations

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