Free Knot Splines with RJMCMC in Survival Data Analysis

The B-spline representation is a common tool to improve the fitting of smooth nonlinear functions, it offers a fitting as a piecewise polynomial. The regions that define the pieces are separated by a sequence of knots. The main difficulty in this type of modeling is the choice of the number and the locations of these knots. The Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm provides a solution to simultaneously select these two parameters by considering the knots as free parameters. This algorithm belongs to the MCMC techniques that allow simulations from target distributions on spaces of varying dimension. The aim of the present investigation is to use this algorithm in the framework of the analysis of survival time, for the Cox model in particular. In fact, the relation between the hazard ratio function and the covariates being assumed to be log-linear, this assumption is too restrictive. Thus, we propose to use the RJMCMC algorithm to model the log hazard ratio function by a B-spline representation with an unknown number of knots at unknown locations. This method is illustrated with two real data sets: the Stanford heart transplant data and lung cancer survival data. Another application of the RJMCMC is selecting the significant covariates, and a simulation study is performed.     

Asymptotic Properties for a Class of Partially Identified Models

We propose inference procedures for partially identified population features for which the population identification region can be written as a transformation of the Aumann expectation of a properly defined set valued random variable (SVRV). An SVRV is a mapping that associates a set (rather than a real number) with each element of the sample space. Examples of population features in this class include interval‐identified scalar parameters, best linear predictors with interval outcome data, and parameters of semiparametric binary models with interval regressor data. We extend the analogy principle to SVRVs and show that the sample analog estimator of the population identification region is given by a transformation of a Minkowski average of SVRVs. Using the results of the mathematics literature on SVRVs, we show that this estimator converges in probability to the population identification region with respect to the Hausdorff distance. We then show that the Hausdorff distance and the directed Hausdorff distance between the population identification region and the estimator, when properly normalized by , converge in distribution to functions of a Gaussian process whose covariance kernel depends on parameters of the population identification region. We provide consistent bootstrap procedures to approximate these limiting distributions. Using similar arguments as those applied for vector valued random variables, we develop a methodology to test assumptions about the true identification region and its subsets. We show that these results can be used to construct a confidence collection and a directed confidence collection. Those are (respectively) collection of sets that, when specified as a null hypothesis for the true value (a subset of values) of the population identification region, cannot be rejected by our tests.     

Italian Adult Gambling Behavior: At Risk and Problem Gambler Profiles

The present study examined adult gambling behaviours from a local perspective in order to assess the adult at risk and problem gambler’s profile stratified by genre and by different forms of game. 4773 Italian adults from 18 to 94 years old were administered a survey to assess socio-cultural information related to gambling behaviour and the SOGS to evaluate gambling behaviour severity. Logistic regression evidenced that both at risk and problem gamblers are associated with male gender, players that use to play to more than one game, gambling with strategy-based games. People with a gambler father or both parents who used to gamble were significantly more associated with problem gambling behaviour than participants with non-gambler parents. These results present adult profiles of at risk and problem providing a more clear understanding about the relationships between gambling behavior severity and type of gambling.     

A Second Best Theory of Institutional Quality

This paper illustrates a theory of the second best where the constraints to the achievement of the optimum are of institutional nature. We consider the effects of corruption and bad governance on the public decision to privatize the provision of a service when contracts are incomplete and there is asymmetric information. We show that both corruption and bad governance are detrimental to welfare, but that removing only one of the two is not necessarily beneficial if the other is still present. The theory supplies a possible explanation to the controversial empirical evidence on the economic effects of corruption.     

Unsupervised Curve Clustering using B-Splines

Data in many different fields come to practitioners through a process naturally described as functional. Although data are gathered as finite vector and may contain measurement errors, the functional form have to be taken into account. We propose a clustering procedure of such data emphasizing the functional nature of the objects. The new clustering method consists of two stages: fitting the functional data by B‐splines and partitioning the estimated model coefficients using a k‐means algorithm. Strong consistency of the clustering method is proved and a real‐world example from food industry is given.     

Firm outsourcing decisions: evidence from U.S. foreign trade zones

This article examines the operations of firms located in U.S. foreign trade subzones to study the responsiveness of outsourcing to international cost changes. I find that firms reduce their reliance on foreign inputs when dollar depreciation increases the relative price of imported inputs. The effect is pervasive across industries and is economically significant. In addition, firms that rely more heavily on imported intermediate inputs reduce their overall shipments when dollar depreciation elevates their imported, input costs. However, the magnitude of the shipments effect is economically small, suggesting that firms respond to exchange rate movements by adjusting their operations on other dimensions.     

Social support needs of families adopting special needs children

A grounded theory begins to explain parental practices in seeking social support. Interviews with 20 parents who had adopted children with special needs and 5 health care providers indicated that parents lacked awareness of available resources; preferred informal support systems; sought help only when "failing" to solve problems; and perceived that health care providers lacked knowledge of adoption issues, family dynamics, and common health issues. Parents preferred informal to formal supports, and felt upset when formal supports were judged ineffective or non-existent.     

Correlates of Attitudes Toward Personal Aging in Older Assisted Living Residents

This study explored factors contributing to older adults’ self-perceptions about their own aging in assisted living (AL) communities. Data analysis was completed based on interviews with 150 older residents from 17 AL communities. Multiple regression analyses found that functional disability and hearing impairment negatively affected attitudes toward personal aging among AL residents, and satisfaction with social support positively influenced attitudes. Health perception mediated attitudes toward personal aging. Findings suggest the importance of social workers helping older AL residents recognize social support as a means of promoting their positive self-regard.     

Sharp Identification Regions in Models With Convex Moment Predictions

We provide a tractable characterization of the sharp identification region of the parameter vector θ in a broad class of incomplete econometric models. Models in this class have set‐valued predictions that yield a convex set of conditional or unconditional moments for the observable model variables. In short, we call these models with convex moment predictions. Examples include static, simultaneous‐move finite games of complete and incomplete information in the presence of multiple equilibria; best linear predictors with interval outcome and covariate data; and random utility models of multinomial choice in the presence of interval regressors data. Given a candidate value for θ, we establish that the convex set of moments yielded by the model predictions can be represented as the Aumann expectation of a properly defined random set. The sharp identification region of θ, denoted Θ_I, can then be obtained as the set of minimizers of the distance from a properly specified vector of moments of random variables to this Aumann expectation. Algorithms in convex programming can be exploited to efficiently verify whether a candidate θ is in Θ_I. We use examples analyzed in the literature to illustrate the gains in identification and computational tractability afforded by our method.     

Effect of a web-based positive psychology intervention on prenatal well-being: A case series study

Background

Detrimental effects of women’s negative feelings during pregnancy have been extensively examined and documented, but research on the influence of positive feelings and protective factors on their prenatal mental health is scarce. Evidence from the positive psychology field has shown that practicing some brief positive exercises, called positive psychology interventions, can maximize well-being by increasing positive emotions, engagement, and meaning.