Harnessing the reliability of outcome measures

A method is presented for engineering the necessary levels of measurement reliability for evaluating ongoing programs. Two studies of levels of client functioning at a community mental health center (CMHC), in which an outcome measure, the Global Assessment Scale (GAS), did not perform as expected, drew attention to the need for better control of outcome measure reliabilities. Drawing from generalizability theory, a study was conducted of three sources of GAS score variance — clients, raters, and training in the use of the scale. Several estimates of reliability (ERs) were developed, depending on the manner in which the GAS ratings were, or would be, obtained in the CMHC. The differences among these ERs clarified why the GAS had lower reliability when used in our setting. Finally, two hypothetical examples are described to illustrate the utility of applying generalizability theory to achieve higher reliabilities for outcome measures.     

Consumer Behaviour and Order Fulfilment in Online Retailing: A Systematic Review

This paper provides a systematic review of consumer behaviour and order fulfilment in online retailing. The objective of this review is threefold: first, to identify elements of order‐fulfilment operations that are relevant to online consumer behaviour (purchase, repurchase, product return); second, to understand the relationship between order‐fulfilment performance and consumer behaviour; and third, to inspire future research on developing consumer service strategies that takes account of these behavioural responses to order‐fulfilment performance outcomes. The paper is based on a systematic review of literature on online consumer behaviour and order‐fulfilment operations, mainly in the fields of marketing and operations, published in international peer‐reviewed journals between 2000 and September 2015. This study indicates that the current literature on online consumer behaviour focuses mainly on the use of marketing tools to improve consumer service levels. Very little research has been conducted on the use of consumer service instruments to steer consumer behaviour or, consequently, to manage related order‐fulfilment activities better. The study culminates in a framework that encompasses elements of order‐fulfilment operations and their relationship to online consumer behaviour. This paper is the first comprehensive review of online consumer behaviour that takes aspects of order‐fulfilment operations into account from both marketing and operations perspectives.     

A second-order iterated smoothing algorithm

Simulation-based inference for partially observed stochastic dynamic models is currently receiving much attention due to the fact that direct computation of the likelihood is not possible in many practical situations. Iterated filtering methodologies enable maximization of the likelihood function using simulation-based sequential Monte Carlo filters. Doucet et al. (2013) developed an approximation for the first and second derivatives of the log likelihood via simulation-based sequential Monte Carlo smoothing and proved that the approximation has some attractive theoretical properties. We investigated an iterated smoothing algorithm carrying out likelihood maximization using these derivative approximations. Further, we developed a new iterated smoothing algorithm, using a modification of these derivative estimates, for which we establish both theoretical results and effective practical performance. On benchmark computational challenges, this method beat the first-order iterated filtering algorithm. The method’s performance was comparable to a recently developed iterated filtering algorithm based on an iterated Bayes map. Our iterated smoothing algorithm and its theoretical justification provide new directions for future developments in simulation-based inference for latent variable models such as partially observed Markov process models.     

First hitting time of integral diffusions and applications

We study the first hitting time of integral functionals of time-homogeneous diffusions, and characterize their Laplace transforms through a stochastic time change. We obtain explicit expressions of the Laplace transforms for the geometric Brownian motion (GBM) and the mean-reverting GBM process. We also introduce a novel probability identity based on an independent exponential randomization and obtain explicit Laplace transforms of the price of arithmetic Asian options and other derivative prices that non-linearly depend on the integral diffusions. Numerical examples are given to demonstrate the accuracy and efficiency of the proposed method.     

Slowing time: Markov-modulated Brownian motions with a sticky boundary

We define a new family of stochastic processes called Markov modulated Brownian motions with a sticky boundary at zero. Intuitively, each process is a regulated Markov-modulated Brownian motion whose boundary behavior is modified to slow down at level zero.

To determine the stationary distribution of a sticky MMBM, we follow a Markov-regenerative approach similar to the one developed with great success in the context of quasi-birth-and-death processes and fluid queues. Our analysis also relies on recent work showing that Markov-modulated Brownian motions arise as limits of a parametrized family of fluid queues.     

Stress and tobacco use among African-American adolescents: the buffering effect of cultural factors

Tobacco is a leading contributor to morbidity and mortality and a primary reason for health disparities among African Americans. In this study we explore the role of stress in smoking and cultural factors that protect against stress among African-American adolescents. Our sample consisted of 239 youth who were recruited into the study while enrolled in 8th and 12th grade. Measures of risk factors (stress, school transition stress, and community disorganization), moderator or protective factors (religious support and intergenerational connections), and 30-day tobacco use were collected. Hierarchical multiple regression analyses were conducted. Intergenerational connections moderated the effect of stress on past 30-day tobacco use. Religious support moderated the effect of neighborhood disorganization on past 30-day tobacco use. Religious support also moderated the effect of stress on past 30-day tobacco use. The findings have implications for prevention efforts to consider religious beliefs and practices and also to link youth with supportive adults in their community.     

Bayesian adaptive lasso with variational Bayes for variable selection in high-dimensional generalized linear mixed models

This article describes a full Bayesian treatment for simultaneous fixed-effect selection and parameter estimation in high-dimensional generalized linear mixed models. The approach consists of using a Bayesian adaptive Lasso penalty for signal-level adaptive shrinkage and a fast Variational Bayes scheme for estimating the posterior mode of the coefficients. The proposed approach offers several advantages over the existing methods, for example, the adaptive shrinkage parameters are automatically incorporated, no Laplace approximation step is required to integrate out the random effects. The performance of our approach is illustrated on several simulated and real data examples. The algorithm is implemented in the R package glmmvb and is made available online.