Estimating the grid of time-points for the piecewise exponential model

One of the greatest challenges related to the use of piecewise exponential models (PEMs) is to find an adequate grid of time-points needed in its construction. In general, the number of intervals in such a grid and the position of their endpoints are ad-hoc choices. We extend previous works by introducing a full Bayesian approach for the piecewise exponential model in which the grid of time-points (and, consequently, the endpoints and the number of intervals) is random. We estimate the failure rates using the proposed procedure and compare the results with the non-parametric piecewise exponential estimates. Estimates for the survival function using the most probable partition are compared with the Kaplan-Meier estimators (KMEs). A sensitivity analysis for the proposed model is provided considering different prior specifications for the failure rates and for the grid. We also evaluate the effect of different percentage of censoring observations in the estimates. An application to a real data set is also provided. We notice that the posteriors are strongly influenced by prior specifications, mainly for the failure rates parameters. Thus, the priors must be fairly built, say, really disclosing the expert prior opinion.     

Penalized Pseudolikelihood Inference in Spatial Interaction Models with Covariates

Given spatially located observed random variables ( x , z = {( x _i , z _i )} _i , we propose a new method for non-parametric estimation of the potential functions of a Markov random field p ( x | z ), based on a roughness penalty approach. The new estimator maximizes the penalized log-pseudolikelihood function and is a natural cubic spline. The calculations involved do not rely on Monte Carlo simulation. We suggest the use of B-splines to stabilize the numerical procedure. An application in Bayesian image reconstruction is described.     

The differential impact of friendship on cooperative and competitive coordination

Theory and Decision - Friendship is commonly assumed to reduce strategic uncertainty and enhance tacit coordination. However, this assumption has never been tested across two opposite poles of...     

Exploring Employee Perceptions towards Smart Working during the COVID-19 Pandemic: a Comparative Analysis of Two Italian Public Research Organizations

Public Organization Review - Of all the socio-economic changes caused by the Covid-19 pandemic, the disruption to workforce organizations will probably leave the largest indelible mark. The way...     

Race and the beauty premium: Mechanical Turk workers’ evaluations of Twitter accounts

ABSTRACT

Race, gender, and physical attractiveness strongly affect perceptions of trustworthiness and subsequent face-to-face interactions. This study examines how social media users’ perceived gender, race, and physical attractiveness can impact their standing online. We test these broad hypotheses by having Amazon Mechanical Turk (MTurk) workers evaluate a sample of 816 Twitter accounts. Our results show a ‘beauty premium,’ where MTurk workers say they are more likely to follow Twitter accounts with attractive pro?le photos, and attractive photos are positively associated with evaluations of trust. However, very attractive Black male and female Twitter accounts are associated with lower evaluations of trust compared to their White counterparts. These findings suggest that social media users’ social characteristics, perceived from their username or profile image, can replicate offline inequality online.     

Explaining performing R&D through alliances: Implications for the business model of Italian dedicated biotech firms

In recent years management scholars and practitioners have been interested in Research and Development (R&D) partnering, especially in high-tech industries. While the motivations of research partnership formation have been widely explored in literature, little attempt has been undertaken to examine the effects of research partnerships on R&D productivity and the implications for business models of the new ventures. In this paper we try to shed some light on the business models of the young ventures involved in R&D partnerships and their effect on R&D productivity. Our hypotheses are that experience in previous alliances, repeated partnerships and tight relationships have positive effects on R&D productivity while rigid governance structures and public R&D subsidies have negative effects on such productivity. We discuss how these aspects affect the business models of biotech firms. We test our hypotheses on a sample of 55 Italian DBFs (Dedicated Biotech Firms). Results support our hypotheses. Managerial implications and further issues for future research are discussed.     

A class of dynamic piecewise exponential models with random time grid

A novel fully Bayesian approach for modeling survival data with explanatory variables using the Piecewise Exponential Model (PEM) with random time grid is proposed. We consider a class of correlated Gamma prior distributions for the failure rates. Such prior specification is obtained via the dynamic generalized modeling approach jointly with a random time grid for the PEM. A product distribution is considered for modeling the prior uncertainty about the random time grid, turning possible the use of the structure of the Product Partition Model (PPM) to handle the problem. A unifying notation for the construction of the likelihood function of the PEM, suitable for both static and dynamic modeling approaches, is considered. Procedures to evaluate the performance of the proposed model are provided. Two case studies are presented in order to exemplify the methodology. For comparison purposes, the data sets are also fitted using the dynamic model with fixed time grid established in the literature. The results show the superiority of the proposed model.     

Forecasting with univariate TAR models

The forecasting stage in the analysis of a univariate threshold-autoregressive model, with exogenous threshold variable, has been developed in this paper via the computation of the so-called predictive distributions. The procedure permits one to forecast simultaneously the response and exogenous variables. An important issue in this work is the treatment of eventual missing observations present in the two time series before obtaining forecasts.     

Sexual Prejudice in Sport Scale: A New Measure

ABSTRACT

This study describes the process of developing and validating the Sexual Prejudice in Sport Scale (SPSS), which is a multidimensional instrument developed to assess attitudes toward lesbians and gay men (LG) in sports. The authors conducted two studies: first, to establish the factor structure of the SPSS on 297 heterosexual athletes; and, second, to test the reliability and validity of the resulting 19-item scale on a sample of 311 heterosexual and 160 LG athletes. Exploratory factor analysis of an initial item pool yielded three factors: open rejection, which assesses the blatant prejudice expressed toward LG people; denial of visibility, which evaluates attitude toward the coming out of LG people; and gendering performance, which corresponds to gender stereotypes about performance/skills of LG people. A confirmatory factor analysis was conducted to assess the stability of the SPSS. The authors documented internal consistency, test-retest stability, and convergent/divergent validity. Implications and directions for future research are discussed.