Pareto improvements by Pareto strategic voting under majority voting with risk loving and risk avoiding voters — A note

Voters satisfy maximin or maximax in their choices between sets of alternatives and secure a Pareto improvement by all voting strategically under simple majority voting for particular sincere preferences. Thus the assumption that strategic voting is a bad thing is challenged and the idea that we should reject voting because of the possibility of misrepresentation dismissed.     

Returns to Tenure or Seniority?

This study documents two empirical facts using matched employer–employee data for Denmark and Portugal. First, workers who are hired last, are the first to leave the firm. Second, workers' wages rise with seniority, where seniority is defined as a worker's tenure relative to the tenure of his colleagues. Controlling for tenure, the probability of a worker leaving the firm decreases with seniority. The increase in expected seniority with tenure explains a large part of the negative duration dependence of the separation hazard. Conditional on ten years of tenure, the wage differential between the 10th and the 90th percentiles of the seniority distribution is 1.1–1.4 percentage points in Denmark and 2.3–3.4 in Portugal.     

Outlier Detection in a Circular Regression Model Using COVRATIO Statistic

In this article, we model the relationship between two circular variables using the circular regression models, to be called JS circular regression model, which was proposed by Jammalamadaka and Sarma (1993). The model has many interesting properties and is sensitive enough to detect the occurrence of outliers. We focus our attention on the problem of identifying outliers in this model. In particular, we extend the use of the COVRATIO statistic, which has been successfully used in the linear case for the same purpose, to the JS circular regression model via a row deletion approach. Through simulation studies, the cut-off points for the new procedure are obtained and its power of performance is investigated. It is found that the performance improves when the resulting residuals have small variance and when the sample size gets larger. An example of the application of the procedure is presented using a real dataset.     

Experienced crawlers avoid real and water drop-offs,even when they are walking

Crawling experience was recently linked to crawling and walking infants’ avoidance of falling on real and water cliffs, whereas walking experience had no effect on walkers’ avoidance behavior (Burnay et al., 2021). In the current study, the behavior of 25 infants was analyzed on the Real Cliff/Water Cliff apparatus using a longitudinal study design. Infants were tested as experienced crawlers (M_crawling = 2.93 months, SD = 1.07), novice walkers (M_walking = 0.68 months, SD = 0.29), and experienced walkers (M_walking = 4.90 months, SD = 0.92). Infants avoided falling on both cliffs when tested as experienced crawlers and their behavior was not different when tested as novice or experienced walkers. These findings confirmed the effect of crawling experience on crawling and walking infants’ avoidance of falls from heights and into water and the transfer of perceptual learning from crawling to walking postures.     

Effect of individual observations on the Box–Cox transformation

In this paper, we consider the influence of individual observations on inferences about the Box–Cox power transformation parameter from a Bayesian point of view. We compare Bayesian diagnostic measures with the ‘forward’ method of analysis due to Riani and Atkinson. In particular, we look at the effect of omitting observations on the inference by comparing particular choices of transformation using the conditional predictive ordinate and the k _d measure of Pettit and Young. We illustrate the methods using a designed experiment. We show that a group of masked outliers can be detected using these single deletion diagnostics. Also, we show that Bayesian diagnostic measures are simpler to use to investigate the effect of observations on transformations than the forward search method.     

The BIAS of the maximum likelihood estimators of the von mises-fisher concentration parameters

It is shown that the bias of the maximum likelihood estimators of the concentration parameters of the von Mises and Fisher distributions is considerable. New estimators which are either nearly unbiased or nearly median unbiased are proposed.     

Can Rural Development Be Measured? Design and Application of a Synthetic Index to Portuguese Municipalities

Social Indicators Research - Starting the discussion about the framework 2021–2027, and regarding the increasing importance given to rural areas by the European Union, multifunctionality of...     

The Rayleigh–Lindley model: properties and applications

In this paper, the Rayleigh–Lindley (RL) distribution is introduced, obtained by compounding the Rayleigh and Lindley discrete distributions, where the compounding procedure follows an approach similar to the one previously studied by Adamidis and Loukas in some other contexts. The resulting distribution is a two-parameter model, which is competitive with other parsimonious models such as the gamma and Weibull distributions. We study some properties of this new model such as the moments and the mean residual life. The estimation was approached via EM algorithm. The behavior of these estimators was studied in finite samples through a simulation study. Finally, we report two real data illustrations in order to show the performance of the proposed model versus other common two-parameter models in the literature. The main conclusion is that the model proposed can be a valid alternative to other competing models well established in the literature.     

A Comparison of Significance Testing Procedures for the Intraclass Correlation from Family Data

Different procedures for testing problems concerning intraclass correlation from familial data are considered in the case of varying number of siblings per family. Under the assumption of multivariate normality, the hypotheses that the intraclass correlation is equal to a specified value are tested. To assess the performance of the tests, Monte Carlo simulations are designed to compare their powers. The Neyman's (1959) C(α) test and the test based on the modified ANOVA F statistic are shown to be consistently more powerful than other procedures.