Conditional covariance penalties for mixed models

The prediction error for mixed models can have a conditional or a marginal perspective depending on the research focus. We introduce a novel conditional version of the optimism theorem for mixed models linking the conditional prediction error to covariance penalties for mixed models. Different possibilities for estimating these conditional covariance penalties are introduced. These are bootstrap methods, cross-validation, and a direct approach called Steinian. The behavior of the different estimation techniques is assessed in a simulation study for the binomial-, the t-, and the gamma distribution and for different kinds of prediction error. Furthermore, the impact of the estimation techniques on the prediction error is discussed based on an application to undernutrition in Zambia.     

Nonparametric beta kernel estimator for long and short memory time series

In this article we introduce a nonparametric estimator of the spectral density by smoothing the periodogram using beta kernel density. The estimator is proved to be bounded for short memory data and diverges at the origin for long memory data. The convergence in probability of the relative error and Monte Carlo simulations show that the proposed estimator automatically adapts to the long- and the short-range dependency of the process. A cross-validation procedure is studied in order to select the nuisance parameter of the estimator. Illustrations on historical as well as most recent returns and absolute returns of the S&P500 index show the performance of the beta kernel estimator. The Canadian Journal of Statistics 48: 582–595; 2020 © 2020 Statistical Society of Canada     

Likelihood-free approximate Gibbs sampling

Likelihood-free methods such as approximate Bayesian computation (ABC) have extended the reach of statistical inference to problems with computationally intractable likelihoods. Such approaches perform well for small-to-moderate dimensional problems, but suffer a curse of dimensionality in the number of model parameters. We introduce a likelihood-free approximate Gibbs sampler that naturally circumvents the dimensionality issue by focusing on lower-dimensional conditional distributions. These distributions are estimated by flexible regression models either before the sampler is run, or adaptively during sampler implementation. As a result, and in comparison to Metropolis-Hastings-based approaches, we are able to fit substantially more challenging statistical models than would otherwise be possible. We demonstrate the sampler’s performance via two simulated examples, and a real analysis of Airbnb rental prices using a intractable high-dimensional multivariate nonlinear state-space model with a 36-dimensional latent state observed on 365 time points, which presents a real challenge to standard ABC techniques.     

A non‐stationary bivariate INAR(1) process with a simple cross‐dependence: Estimation with some properties

This paper considers modelling of a non‐stationary bivariate integer‐valued autoregressive process of order 1 (BINAR(1)) where the cross‐dependence between the counting series is formed through the relationship of the current series with the previous‐lagged count series observations while the pair of innovations is independent and marginally Poisson. In addition, this paper proposes a generalised quasi‐likelihood (GQL) estimating equation based on the exact specification of the mean score and the auto‐covariance structure. The proposed approach is also compared with other popular techniques such as conditional maximum likelihood (CML), generalised least squares (GLS) and generalised method of moment (GMM) based on simulated data from the proposed BINAR(1). Moreover, the model is applied to weekly series of day and night road accidents arising in some regions of Mauritius and is compared with other existing BINAR(1) models.     

Public income transfers and wealth accumulation at the bottom: Within and between country differences in Canada and the United States

Both Canada and the United States are considered liberal welfare states, yet exhibit notable differences in income poverty attributed to social policy. While a more generous welfare system lifts many above income poverty, models of household financial behaviour suggest that more income from the state should displace private savings via a substitution effect. Using nationally representative wealth surveys from Canada and the US from 1998/1999 to 2016 we extend knowledge on the relationship between the welfare state and private wealth accumulation. Specifically, we study household asset poverty defined as financial asset levels that fall below three-month adjusted income poverty threshold. Asset poverty rates varied over time in the two countries and were higher in the less generous US welfare state. Further, income transfer share was positively related to asset poverty in Canada but not in the US. Counterfactual estimates offered evidence of the substitution effect in Canada, where higher levels of transfers may crowd out private asset accumulation. Results invite further consideration of the concept of asset poverty and its relationship to welfare state characteristics.     

Reactive and proactive aggression in children with and without ADHD and negative emotional lability

Children with attention‐deficit/hyperactivity disorder (ADHD) exhibit marked difficulty with frequent and intense shifts in negative or positive emotions, referred to as emotional lability. This study examines the relationships between negative emotional lability (NEL), ADHD, and reactive and proactive aggression. A community sample of 96 children (53 with ADHD, 43 control) ages 8–12 and their parents completed a baseline session include a diagnostic structured interview and the Reactive–Proactive Aggression Questionnaire (RPAQ). Parents then rated their child's NEL using the Positive and Negative Affect Scale as part of an Ecological Momentary Assessment protocol 3 times a day for 28 days. Seventy‐three participants (37 with ADHD, 36 control) completed the RPAQ at follow‐up. Hierarchical regression analyses indicated that NEL significantly and independently estimates reactive aggression at baseline and follow‐up, regardless of ADHD status. NEL and ADHD status significantly estimates concurrent proactive aggression, but not at follow‐up. Results of this study suggest that NEL accounts for a significant amount of the variability in reactive aggression, a common difficulty in children with ADHD. Further, individuals that are reportedly emotionally labile become more reactively aggressive over time. Both ADHD diagnostic and NEL are predictors of proactive aggression at baseline, although no interaction occurred.     

Sexual Minorities,Social Context,and Union Formation

Population Research and Policy Review - The current study examines the association between social context and the formation of same-sex coresidential unions, with a particular focus on sexual...     

Money and Happiness: Income,Wealth and Subjective Well-Being

Social Indicators Research - We examine the complex relationship between money and happiness. We find that both permanent income and wealth are better predictors of life satisfaction than current...     

Between Neo-liberalism and Neo-indigenismo: Reconstructing National Identity in Mexico

Heightened integration with the US coupled with the rise of an indigenous movement have challenged and strained long-held images regarding the US and the indigenous within Mexico. As a result, Mexico finds itself facing the difficult task of re-evaluating and reconstructing its national identity. This paper explores the traditional images of the US and the Indian shaping national identity in Mexico and the challenges posed today by neo-liberalism and neo-indigenismo. It sets out a range of current issues, explores the linkages and contradictions, and examines the state of national reform. Fundamentally, the paper strives to raise important theoretical questions and hence set the stage for further research into these issues.