Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

The exponentiated power exponential regression model with different regression structures: application in nursing data

We define the exponentiated power exponential distribution and propose a regression model with different systematic structures based on the new distribution. We show that the new regression model can be applied to dispersion data since it represents a parametric family of models that includes as sub-models some widely-known regression models. It then can be used more effectively in the analysis of real data. We use maximum likelihood estimation and derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. Some global-influence measurements are also investigated and simulation studies are performed to evaluate the accuracy of the estimates. We provide an application of the regression model with four systematic structures to nursing activities score data in the Unit of the Medical Clinic of University of São Paulo (USP) Hospital.     

A new infinitely divisible discrete distribution with applications to count data modeling

A new discrete distribution involving geometric and discrete Pareto as special cases is introduced. The distribution possesses many interesting properties like decreasing hazard rate, zero vertex uni-modality, over-dispersion, infinite divisibility and compound Poisson representation, which makes the proposed distribution well suited for count data modeling. Other issues including closure property under minima, comparison of its distribution tail with other distributions via actuarial indices are discussed. The method of proportion and maximum likelihood method are presented for parameter estimation. Finally the performance of the proposed distribution over other classical and newly proposed infinitely divisible distributions are discussed.     

Tempered Mittag-Leffler Lévy processes

In this article, we introduce tempered Mittag-Leffler Lévy processes (TMLLP). TMLLP is represented as tempered stable subordinator delayed by a gamma process. Its probability density function and Lévy density are obtained in terms of infinite series and Mittag-Leffler function, respectively. Asymptotic forms of the tails and moments are given. A step-by-step procedure of the parameters estimation and simulation of sample paths is given. We also provide main results available for Mittag-Leffler Lévy processes (MLLP) and some extensions which are not available in a collective way in a single article. Our results generalize and complement the results available on Mittag-Leffler distribution and MLLP in several directions. Further, the asymptotic forms of the moments of the first-exit times of the TMLLP are also discussed.     

A semiparametric approach for modelling multivariate nonlinear time series

In this article, a semiparametric time‐varying nonlinear vector autoregressive (NVAR) model is proposed to model nonlinear vector time series data. We consider a combination of parametric and nonparametric estimation approaches to estimate the NVAR function for both independent and dependent errors. We use the multivariate Taylor series expansion of the link function up to the second order which has a parametric framework as a representation of the nonlinear vector regression function. After the unknown parameters are estimated by the maximum likelihood estimation procedure, the obtained NVAR function is adjusted by a nonparametric diagonal matrix, where the proposed adjusted matrix is estimated by the nonparametric kernel estimator. The asymptotic consistency properties of the proposed estimators are established. Simulation studies are conducted to evaluate the performance of the proposed semiparametric method. A real data example on short‐run interest rates and long‐run interest rates of United States Treasury securities is analyzed to demonstrate the application of the proposed approach. The Canadian Journal of Statistics 47: 668–687; 2019 © 2019 Statistical Society of Canada     

The effect of small sample on optimal designs for logistic regression models

Asymptotic methods are commonly used in statistical inference for unknown parameters in binary data models. These methods are based on large sample theory, a condition which may be in conflict with small sample size and hence leads to poor results in the optimal designs theory. In this paper, we apply the second order expansions of the maximum likelihood estimator and derive a matrix formula for the mean square error (MSE) to obtain more precise optimal designs based on the MSE. Numerical results indicate the new optimal designs are more efficient than the optimal designs based on the information matrix.     

Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective

Statistics and Computing - We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with nonlinear...     

Transnational queer sociological analysis of sexual identity and civic‐political activism in Hong Kong,Taiwan and mainland China

The sociology of homosexuality lacks engagement with queer theory and postcolonialism and focuses primarily on the global metropoles, thus failing to provide a plausible account of non‐Western non‐normative sexual identities. This research adopts the author’s newly proposed transnational queer sociology to address these deficiencies. First, it critiques the Western model of sexual identity predominantly employed to elucidate non‐Western, non‐normative sexualities. It does so by examining not only the queer flows between West and non‐West but also those among and within non‐Western contexts to produce translocally shared and mutually referenced experiences. Second, the proposed approach combines sociology with queer theory by emphasizing the significant role of material, as well as discursive, analyses in shaping queer identities, desires and practices. This article employs the approach to examine young gay male identities, as revealed in 90 in‐depth interviews conducted in Hong Kong (n = 30), Taiwan (Taipei, n = 30) and mainland China (Shanghai, n = 30) between 2017 and 2019. More specifically, it highlights the interplay between the state and identity by investigating the intersection and intertwining effects of these young men’s sexual and cultural/national identities, revealing three different forms of civic‐political activism. The article both demonstrates the way in which sexuality and the state are mutually constituted and provides nuanced analysis of the heterogeneity of contemporary homosexualities in Hong Kong, Taiwan and mainland China. In applying a new sociological approach to understanding sexuality, this research joins the growing body of scholarship within sociology that is decentring the Western formation of universal knowledge.     

Predatory purchasing practices in global apparel supply chains and the employment relations squeeze in the Indian garment export industry

While the literature has long acknowledged worker rights abuses in global apparel supply chains, less attention has been paid to the recent worsening of certain decent work deficits and its causes, which this article links to predatory purchasing practices arising from growing power asymmetries. These practices, illustrated here by the garment export industry in India, exert downward pressure on wages and incite non‐standard forms of employment and greater verbal and physical abuse as productivity demands rise. They can be addressed through pricing mechanisms that cover the cost of decent work, appropriate State regulation and worker involvement.     

Establishing Safety in Emotionally Focused Couple Therapy: A Single‐Case Process Study

Emotionally Focused Couple Therapy (EFT) is an effective approach to working with distressed couples. It is not only effective in reducing relationship distress, but also in successfully maintaining gains over time. In the current study, we sought to understand the therapy processes that result in the creation of safety during stage one of EFT. Safety is a key ingredient in stage one, setting the stage for deeper interpersonal and intrapsychic work in later stages of treatment. In order to achieve the study aim, we analyzed video recordings of the first six sessions of a single couple case from an EFT randomized controlled trial. Using a theory building case study approach, results reveal that all expected components related to the creation of safety were achieved. Additional findings from our analyses indicate that the therapist was highly active, especially in seizing key moments in the therapy provided by this particular case, which shifted the couple in the direction of safety and deescalated their interactional cycle. Implications of key findings are discussed.