scholarly journals Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis

2018 ◽  
Vol 11 (3) ◽  
pp. 52 ◽  
Author(s):  
Mark Jensen ◽  
John Maheu
Keyword(s):  
Dirichlet Process ◽  
Joint Distribution ◽  
Nonparametric Model ◽  
Bayesian Nonparametric ◽  
Excess Returns ◽  
Dirichlet Process Prior ◽  
Stock Market Returns ◽  
Conditional Mean ◽  
Risk Return ◽  
Volatility Feedback

In this paper, we let the data speak for itself about the existence of volatility feedback and the often debated risk–return relationship. We do this by modeling the contemporaneous relationship between market excess returns and log-realized variances with a nonparametric, infinitely-ordered, mixture representation of the observables’ joint distribution. Our nonparametric estimator allows for deviation from conditional Gaussianity through non-zero, higher ordered, moments, like asymmetric, fat-tailed behavior, along with smooth, nonlinear, risk–return relationships. We use the parsimonious and relatively uninformative Bayesian Dirichlet process prior to overcoming the problem of having too many unknowns and not enough observations. Applying our Bayesian nonparametric model to more than a century’s worth of monthly US stock market returns and realized variances, we find strong, robust evidence of volatility feedback. Once volatility feedback is accounted for, we find an unambiguous positive, nonlinear, relationship between expected excess returns and expected log-realized variance. In addition to the conditional mean, volatility feedback impacts the entire joint distribution.

scholarly journals A Bayesian Nonparametric Model for Inferring Subclonal Populations from Structured DNA Sequencing Data

2020 ◽  
Author(s):  
Shai He ◽  
Aaron Schein ◽  
Vishal Sarsani ◽  
Patrick Flaherty
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Dirichlet Process ◽  
Lymphoblastic Leukemia ◽  
Nonparametric Model ◽  
Dirichlet Process Mixture ◽  
Sequencing Data ◽  
Hierarchical Dirichlet Process ◽  
Dirichlet Process Prior

There are distinguishing features or “hallmarks” of cancer that are found across tumors, individuals, and types of cancer, and these hallmarks can be driven by specific genetic mutations. Yet, within a single tumor there is often extensive genetic heterogeneity as evidenced by single-cell and bulk DNA sequencing data. The goal of this work is to jointly infer the underlying genotypes of tumor subpopulations and the distribution of those subpopulations in individual tumors by integrating single-cell and bulk sequencing data. Understanding the genetic composition of the tumor at the time of treatment is important in the personalized design of targeted therapeutic combinations and monitoring for possible recurrence after treatment.We propose a hierarchical Dirichlet process mixture model that incorporates the correlation structure induced by a structured sampling arrangement and we show that this model improves the quality of inference. We develop a representation of the hierarchical Dirichlet process prior as a Gamma-Poisson hierarchy and we use this representation to derive a fast Gibbs sampling inference algorithm using the augment-and-marginalize method. Experiments with simulation data show that our model outperforms standard numerical and statistical methods for decomposing admixed count data. Analyses of real acute lymphoblastic leukemia cancer sequencing dataset shows that our model improves upon state-of-the-art bioinformatic methods. An interpretation of the results of our model on this real dataset reveals co-mutated loci across samples.

scholarly journals A Bayesian nonparametric model for textural pattern heterogeneity

2021 ◽  
Vol 70 (2) ◽  
pp. 459-480
Author(s):  
Xiao Li ◽  
Michele Guindani ◽  
Chaan S. Ng ◽  
Brian P. Hobbs
Keyword(s):  
Nonparametric Model ◽  
Bayesian Nonparametric

scholarly journals The comparison of the scores obtained by Bayesian nonparametric model and classical test theory methods

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110283
Author(s):  
Meltem Yurtcu ◽  
Hülya Kelecioglu ◽  
Edward L Boone
Keyword(s):  
Classical Test Theory ◽  
Small Sample ◽  
Test Theory ◽  
Nonparametric Model ◽  
Bayesian Nonparametric ◽  
Test Equating ◽  
Classical Test ◽  
A Value ◽  
Item Functioning ◽  
The Individual

Bayesian Nonparametric (BNP) modelling can be used to obtain more detailed information in test equating studies and to increase the accuracy of equating by accounting for covariates. In this study, two covariates are included in the equating under the Bayes nonparametric model, one is continuous, and the other is discrete. Scores equated with this model were obtained for a single group design for a small group in the study. The equated scores obtained with the model were compared with the mean and linear equating methods in the Classical Test Theory. Considering the equated scores obtained from three different methods, it was found that the equated scores obtained with the BNP model produced a distribution closer to the target test. Even the classical methods will give a good result with the smallest error when using a small sample, making equating studies valuable. The inclusion of the covariates in the model in the classical test equating process is based on some assumptions and cannot be achieved especially using small groups. The BNP model will be more beneficial than using frequentist methods, regardless of this limitation. Information about booklets and variables can be obtained from the distributors and equated scores that obtained with the BNP model. In this case, it makes it possible to compare sub-categories. This can be expressed as indicating the presence of differential item functioning (DIF). Therefore, the BNP model can be used actively in test equating studies, and it provides an opportunity to examine the characteristics of the individual participants at the same time. Thus, it allows test equating even in a small sample and offers the opportunity to reach a value closer to the scores in the target test.

scholarly journals Bayesian Nonparametric Model for the Validation of Peptide Identification in Shotgun Proteomics

2008 ◽  
Vol 8 (3) ◽  
pp. 547-557 ◽  
Author(s):  
Jiyang Zhang ◽  
Jie Ma ◽  
Lei Dou ◽  
Songfeng Wu ◽  
Xiaohong Qian ◽  
...  
Keyword(s):  
Peptide Identification ◽  
Shotgun Proteomics ◽  
Nonparametric Model ◽  
Bayesian Nonparametric
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