A latent process model for dementia and psychometric tests

We jointly model longitudinal values of a psychometric test and diagnosis of dementia. The model is based on a continuous-time latent process representing cognitive ability. The link between the latent process and the observations is modeled in two phases. Intermediate variables are noisy observations of the latent process; scores of the psychometric test and diagnosis of dementia are obtained by categorizing these intermediate variables. We propose maximum likelihood inference for this model and we propose algorithms for performing this task. We estimated the parameters of such a model using the data of the 5 year follow-up of the PAQUID study. In particular this analysis yielded interesting results about the effect of educational level on both latent cognitive ability and specific performance in the mini mental test examination. The predictive ability of the model is illustrated by predicting diagnosis of dementia at the 8 year follow-up of the PAQUID study based on the information from the first 5 years.     

Tablet assessment of word comprehension reveals coarse word representations in 18–⁠20-month-old toddlers

The present study explores the viability of using tablets in assessing early word comprehension by means of a two-alternative forced-choice task. Forty-nine 18–20-month-old Norwegian toddlers performed a touch-based word recognition task, in which they were prompted to identify the labeled target out of two displayed items on a touchscreen tablet. In each trial, the distractor item was either semantically related (e.g., dog–cat) or unrelated (e.g., dog–airplane) to the target. Our results show that toddlers as young as 18 months can engage meaningfully with a tablet-based assessment, with minimal verbal instruction and child–administrator interaction. Toddlers performed better in the semantically unrelated condition than in the related condition, suggesting that their word representations are still semantically coarse at this age. Furthermore, parental reports of comprehension, using the Norwegian version of the MacArthur–Bates Communicative Development Inventories, predicted toddlers’ performance, with parent–child agreement stronger in the semantically unrelated condition, indicating that parents declare a word to be known by their child if it is understood at a coarse representational level. This study provides among the earliest evidence that remote data collection in 18-20 month-old toddlers is viable, as comparable results were observed from both in-laboratory and online administration of the touchscreen recognition task.     

PRODUCT MARKET REGULATION,FIRM SELECTION,AND UNEMPLOYMENT

This paper analyzes the effect of product market regulation (PMR) on unemployment in a search model with heterogeneous multiple‐worker firms. In our setup, PMR modifies the distribution of firm productivities, thereby affecting the equilibrium rate of unemployment. We distinguish between PMR related to entry costs and PMR that generates recurrent fixed costs. We find that: (i) higher entry costs raise the rate of unemployment mainly through our novel selection effect, (ii) higher fixed costs decrease unemployment through the selection effect and increase it through the competition effect analyzed in Blanchard and Giavazzi (2003, Quarterly Journal of Economics, 118, 879–907). Firm heterogeneity magnifies the impact of both types of regulatory costs. We propose econometric evidence consistent with the unemployment effects of sunk versus recurring costs.     

Post-crisis learning in public agencies: what do we learn from both actors and institutions?

Post-crisis learning is a challenge for public organizations, and especially for agencies which handle health and environmental risks. This article investigates how the Belgian Food Safety Agency settles mechanisms for drawing lessons from crises while ensuring day-to-day routine. The framework by Crozier and Friedberg is used as a guideline to consider both the actors and the system, both strategic games and institutional constraints. The article helps in understanding the institutional logics underpinning how the public organizations learn from societal risk and crisis. Centralization and openness appear to be guiding principles, resulting from the learning games. They also generate tensions that the actors’ games manage by defining new rules for cooperation. Both the practice (through our case study) and the theory (combining actors and institutions) broaden the lens of policy analysis for what policy-making at organizational level concerns.     

On the use of extreme value tail modeling for generalized pairwise comparisons with censored outcomes

In randomized clinical trials, methods of pairwise comparisons such as the ‘Net Benefit’ or the ‘win ratio’ have recently gained much attention when interests lies in assessing the effect of a treatment as compared to a standard of care. Among other advantages, these methods are usually praised for delivering a treatment measure that can easily handle multiple outcomes of different nature, while keeping a meaningful interpretation for patients and clinicians. For time-to-event outcomes, a recent suggestion emerged in the literature for estimating these treatment measures by providing a natural handling of censored outcomes. However, this estimation procedure may lead to biased estimates when tails of survival functions cannot be reliably estimated using Kaplan–Meier estimators. The problem then extrapolates to the other outcomes incorporated in the pairwise comparison construction. In this work, we suggest to extend the procedure by the consideration of a hybrid survival function estimator that relies on an extreme value tail model through the Generalized Pareto distribution. We provide an estimator of treatment effect measures that notably improves on bias and remains easily apprehended for practical implementation. This is illustrated in an extensive simulation study as well as in an actual trial of a new cancer immunotherapy.     

Contemporaneous Versus Retrospective Reports of Cohabitation in the Fragile Families Survey

We compare contemporaneous and retrospective reports of cohabitation among unmarried mothers in the Fragile Families and Child Wellbeing survey (N =2,524). We find that (a) many mothers revise their reports of whether they cohabited at the time of the birth of their child and (b) revisions in reports are systematically related to individuals’ characteristics and the quality and trajectory of parents’ relationships. These results have important implications for analyses of determinants and consequences of family structure using these data and, potentially, any retrospective data on relationships and family structure.     

Model-based clustering of multivariate ordinal data relying on a stochastic binary search algorithm

We design a probability distribution for ordinal data by modeling the process generating data, which is assumed to rely only on order comparisons between categories. Contrariwise, most competitors often either forget the order information or add a non-existent distance information. The data generating process is assumed, from optimality arguments, to be a stochastic binary search algorithm in a sorted table. The resulting distribution is natively governed by two meaningful parameters (position and precision) and has very appealing properties: decrease around the mode, shape tuning from uniformity to a Dirac, identifiability. Moreover, it is easily estimated by an EM algorithm since the path in the stochastic binary search algorithm can be considered as missing values. Using then the classical latent class assumption, the previous univariate ordinal model is straightforwardly extended to model-based clustering for multivariate ordinal data. Parameters of this mixture model are estimated by an AECM algorithm. Both simulated and real data sets illustrate the great potential of this model by its ability to parsimoniously identify particularly relevant clusters which were unsuspected by some traditional competitors.     

A Test for Comparing Tail Indices for Heavy-Tailed Distributions via Empirical Likelihood

In this work, the problem of testing whether different (?2) independent samples, with (possibly) different heavy-tailed distributions, share the same extreme value index, is addressed. The test statistic proposed is inspired by the empirical likelihood methodology and consists in an ANOVA-like confrontation of Hill estimators. Asymptotic validity of this simple procedure is proved and efficiency, in terms of empirical type I error and power, is investigated through simulations under a variety of situations. Surprisingly, this topic had hardly been addressed before, and only in the two-sample case, though it can prove useful in applications.