Bayesian analysis of a multiple-recapture model

In the Bayesian analysis of a multiple-recapture census, different diffuse prior distributions can lead to markedly different inferences about the population size N. Through consideration of the Fisher information matrix it is shown that the number of captures in each sample typically provides little information about N. This suggests that if there is no prior information about capture probabilities, then knowledge of just the sample sizes and not the number of recaptures should leave the distribution of Nunchanged. A prior model that has this property is identified and the posterior distribution is examined. In particular, asymptotic estimates of the posterior mean and variance are derived. Differences between Bayesian and classical point and interval estimators are illustrated through examples.     

Taxonomy of outsourcing decision models

This article proposes a taxonomy of outsourcing decision models. The proposed taxonomy may assist practitioners in their decision-making and researchers in the identification, evaluation and classification of decision models. Further, this article also provides an overview and evaluation of existing outsourcing decision models, and assesses them for their applicability to the outsourcing decision process. In particular, this article finds that the outsourcing decision models proposed in the literature have limitations in their applicability to the outsourcing decision process.     

Emotional balance and probability weighting

We find suggestive evidence that emotional balance has an impact on probability weighting incremental to demographic controls. Specifically, low negative affectivity (implying high emotional balance) tends to be a characteristic of those whose probability weighting functions exhibit lower curvature and more neutral elevation. In other words, emotional balance seems to push people in the direction of normative expected utility theory.     

Writing the self: Exploring the stigma of hearing impairment

Neoclassical economic and sociological views of discrimination are compared. We summarize economic models of taste, statistical, error, and monopolistic discrimination. Economists argue that competitive market forces should lead to the demise of discrimination in the long run. After explaining these arguments, we present sociological arguments about institutional and social‐psychological mechanisms that promote the persistence of discrimination. A typology of social‐psychological feedback effects from discrimination is presented. We conclude that it is important to recognize forces promoting both the erosion and persistence of discrimination and that this requires a perspective drawing upon both sociology and economics.     

Identifying Latent Structures in Panel Data

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier‐Lasso (C‐Lasso) that serves to shrink individual coefficients to the unknown group‐specific coefficients. C‐Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C‐Lasso also achieves the oracle property so that group‐specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C‐Lasso is preserved in some special cases. Simulations demonstrate good finite‐sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.     

A Business Model Innovation Typology

An effective business model is the core enabler of any company's performance. Business model innovation is not only becoming more and more important due to increasing and globalizing competition, but also an enormous challenge, both theoretically and practically. Although many managers are eager to consider more disruptive changes to their business model, they often do not know how to articulate their existing or desired business model and, even less so, understand the possibilities for innovating it. One of the steps toward developing more theoretical insight and practical guidelines is the identification of types and the development of a typology of business model innovations. Ten retrospective case studies of business model innovations undertaken by two industrial companies provide the empirical basis for this article. We analyzed the characteristics of these innovations as well as their success rates. The findings suggest that there are indeed various business model innovation types, each with its own characteristics and challenges.     

Investigating impacts of complex sampling on latent growth curve modelling

We investigate the impacts of complex sampling on point and standard error estimates in latent growth curve modelling of survey data. Methodological issues are illustrated with empirical evidence from the analysis of longitudinal data on life satisfaction trajectories using data from the British Household Panel Survey, a national representative survey in Great Britain. A multi-process second-order latent growth curve model with conditional linear growth is used to study variation in the two perceived life satisfaction latent factors considered. The benefits of accounting for the complex survey design are considered, including obtaining unbiased both point and standard error estimates, and therefore correctly specified confidence intervals and statistical tests. We conclude that, even for the rather elaborated longitudinal data models that were considered, estimation procedures are affected by variance-inflating impacts of complex sampling.     

Methods to assess measurement error in questionnaires of sedentary behavior

Sedentary behavior has already been associated with mortality, cardiovascular disease, and cancer. Questionnaires are an affordable tool for measuring sedentary behavior in large epidemiological studies. Here, we introduce and evaluate two statistical methods for quantifying measurement error in questionnaires. Accurate estimates are needed for assessing questionnaire quality. The two methods would be applied to validation studies that measure a sedentary behavior by both questionnaire and accelerometer on multiple days. The first method fits a reduced model by assuming the accelerometer is without error, while the second method fits a more complete model that allows both measures to have error. Because accelerometers tend to be highly accurate, we show that ignoring the accelerometer's measurement error, can result in more accurate estimates of measurement error in some scenarios. In this article, we derive asymptotic approximations for the mean-squared error of the estimated parameters from both methods, evaluate their dependence on study design and behavior characteristics, and offer an R package so investigators can make an informed choice between the two methods. We demonstrate the difference between the two methods in a recent validation study comparing previous day recalls to an accelerometer-based ActivPal.