On the role of local blockchain network features in cryptocurrency price formation

Cryptocurrencies and the underpinning blockchain technology have gained unprecedented public attention recently. In contrast to fiat currencies, transactions of cryptocurrencies, such as Bitcoin and Litecoin, are permanently recorded on distributed ledgers to be seen by the public. As a result, public availability of all cryptocurrency transactions allows us to create a complex network of financial interactions that can be used to study not only the blockchain graph, but also the relationship between various blockchain network features and cryptocurrency risk investment. We introduce a novel concept of chainlets, or blockchain motifs, to utilize this information. Chainlets allow us to evaluate the role of local topological structure of the blockchain on the joint Bitcoin and Litecoin price formation and dynamics. We investigate the predictive Granger causality of chainlets and identify certain types of chainlets that exhibit the highest predictive influence on cryptocurrency price and investment risk. More generally, while statistical aspects of blockchain data analytics remain virtually unexplored, the paper aims to highlight various emerging theoretical, methodological and applied research challenges of blockchain data analysis that will be of interest to the broad statistical community. The Canadian Journal of Statistics 48: 561–581; 2020 © 2020 Statistical Society of Canada     

A Comparison of Frailty and Other Models for Bivariate Survival Data

Multivariate survival data arise when eachstudy subject may experience multiple events or when study subjectsare clustered into groups. Statistical analyses of such dataneed to account for the intra-cluster dependence through appropriatemodeling. Frailty models are the most popular for such failuretime data. However, there are other approaches which model thedependence structure directly. In this article, we compare thefrailty models for bivariate data with the models based on bivariateexponential and Weibull distributions. Bayesian methods providea convenient paradigm for comparing the two sets of models weconsider. Our techniques are illustrated using two examples.One simulated example demonstrates model choice methods developedin this paper and the other example, based on a practical dataset of onset of blindness among patients with diabetic Retinopathy,considers Bayesian inference using different models.     

Bayesian dynamic models for survival data with a cure fraction

In this paper, we propose a new class of semi-parametric cure rate models. Specifically, we construct dynamic models for piecewise hazard functions over a finite partition of the time axis. Allowing the size of partition and the levels of baseline hazard to be random, our proposed models provide a great flexibility in controlling the degree of parametricity in the right tail of the survival distribution and the amount of correlations among the log-baseline hazard levels. Several properties of the proposed models are derived, and propriety of the implied posteriors with improper noninformative priors for regression coefficients based on the proposed models is established for the fixed partition of the time axis. In addition, an efficient reversible jump computational algorithm is developed for carrying out posterior computation. A real data set from a melanoma clinical trial is analyzed in detail to further demonstrate the proposed methodology.     

Relationship among differentiation of self, relationship satisfaction, partner support, and depression in patients with chronic lung disease and their partners

The purpose of the present study is to test the relationships among differentiation of self, relationship satisfaction, perceived partner support, and depression in a sample of patients with chronic lung disease (CLD) and their partners. The sample consisted of 52 patients with CLD and their partners. Hierarchical multiple regression was used for data analysis. The results indicate that (a) patients' differentiation of self is significantly lower than partners' differentiation of self, (b) patients' relationship satisfaction is positively correlated with partners' relationship satisfaction, (c) patients' relationship satisfaction predicts perceived partner supportive behavior, (d) perceived partner unsupportive behavior predicts patients' depression, (e) partners' differentiation of self predicts partners' relationship satisfaction, (f) partners' relationship satisfaction predicts partner supportive behavior, and (g) partners' differentiation of self and relationship satisfaction predicts partners' depression. The study serves to highlight the fact that the patient is part of a system, and that taking the system into account is relevant to the well-being of both the patient and the partner, in particular for patients with CLD, a heretofore unstudied population. The results of such studies have significant implications for couple and family therapists.     

A supervised independent-living orientation program for adolescents

A program is described that provides for transition into a relatively unstructured supervised independent-living program for adolescents with a history of behavioral and emotional disturbance, often including delinquency and/or psychiatric hospitalization. Contracts used to define expectations, logistics, and conflicts experienced by the youths are discussed. Developmental issues are highlighted, focusing on the rapprochement subphase.     

Combining coordinates in simultaneous estimation of normal means

The problem of combining coordinates in Stein-type estimators, when simultaneously estimating normal means, is considered. The question of deciding whether to use all coordinates in one combined shrinkage estimator or to separate into groups and use separate shrinkage estimators on each group is considered. A Bayesian viewpoint is (of necessity) taken, and it is shown that the ‘combined’ estimator is, somewhat surprisingly, often superior.     

On Progressively Type-II Censored Two-parameter Rayleigh Distribution

Recently, Rayleigh distribution has received considerable attention in the statistical literature. In this article, we consider the point and interval estimation of the functions of the unknown parameters of a two-parameter Rayleigh distribution. First, we obtain the maximum likelihood estimators (MLEs) of the unknown parameters. The MLEs cannot be obtained in explicit forms, and we propose to use the maximization of the profile log-likelihood function to compute the MLEs. We further consider the Bayesian inference of the unknown parameters. The Bayes’ estimates and the associated credible intervals cannot be obtained in closed forms. We use the importance sampling technique to approximate (compute) the Bayes’ estimates and the associated credible intervals. For comparison purposes, we have also used the exact method to compute the Bayes’ estimates and the corresponding credible intervals. Monte Carlo simulations are performed to compare the performances of the proposed method, and one dataset has been analyzed for illustrative purposes. We further consider the Bayes’ prediction problem based on the observed samples, and provide the appropriate predictive intervals. A data example has been provided for illustrative purposes.     

On estmation of poisson lovits

Simultaneous estimation of poisson logits is considered in a 2xp contingency table under entropy loss. Classical estimators, which are corrected versions of the maximum likeihood estimators, are obtained as generalized Bayes and empirical Bayes estimators. Finally, improved estimators are obtained which domicate the generalized Bayes estimators.     

Bayesian inference and diagnostics in zero-inflated generalized power series regression model

ABSTRACT

The paper provides a Bayesian analysis for the zero-inflated regression models based on the generalized power series distribution. The approach is based on Markov chain Monte Carlo methods. The residual analysis is discussed and case-deletion influence diagnostics are developed for the joint posterior distribution, based on the ψ-divergence, which includes several divergence measures such as the Kullback–Leibler, J-distance, L₁ norm, and χ²-square in zero-inflated general power series models. The methodology is reflected in a data set collected by wildlife biologists in a state park in California.