Behavioral evidence for framing effects in the resolution of the doctrinal paradox

The aggregation of individual sets of judgments over interconnected propositions can yield inconsistent collective sets of judgments, even when the individual sets of judgments are themselves consistent. A doctrinal paradox occurs when majority voting on a compound proposition (such as a conjunction or disjunction) yields a different result than majority voting on each of the elements of the proposition. For example, when most individuals accept proposition X; most individuals accept proposition Y ; but only a minority of individuals accept the compound proposition ‘X and Y’. Conducting two elemental votes would lead to accept X and Y , but conducting one compound vote would lead to reject X and Y . In such a situation, do people manifest a stable preference as to which voting procedure should be applied? This research reports the results of two behavioral experiments using a within-participant design, which show that procedural preferences can be upturned by framing either positively or negatively the set of judgments to be aggregated. This shift in procedural preference leads to large swings in the final collective judgment endorsed by participants.     

Relational sociology,pragmatism, transactions and social fields

Starting from the idea that relational sociology has been founded on various and incompatible social ontologies, I argue that it is at risk of losing its raison d'être if we do not answer two fundamental practical and ontological questions: (1) Why do we need relational sociology? and (2) What do we study in relational sociology? In this respect, I propose a deep, transactional sociology partly and freely inspired by the work of J. Dewey which clearly detaches relational sociology from social determinism and co-determinism.     

Association between sarcopenia and erectile dysfunction in males with type II diabetes mellitus

Background: The prevalence rates for both sarcopenia and erectile dysfunction (ED) gradually increase in middle-aged and elderly diabetic male population and they impair physical functioning, sexual functioning, and quality of life. The aim of the present study was to evaluate the sarcopenia in patients with diabetic ED.

Methods: The study included 98 male patients with type II diabetes mellitus (DM) aged 18–80?years. Blood chemistry and hormone levels were obtained. The International Index of Erectile Function (IIEF-5) questionnaire was administered to the patients. The patients were divided into three groups according to the IIEF-5 score; a score of 5–10 points indicated severe ED, a score of 11–20 indicated moderate ED, and a score of 21–25 points indicated no ED. The muscle mass, handgrip strength, timed up and go test, upper mid-arm circumference, calf circumference, and body mass index were obtained. The statistical analysis was performed using MedCalc Statistical Software version 12.7.7. All parameters were compared between the three groups.

Results: Of 98 patients included in the study, 84 patients had severe sarcopenia, 13 had moderate sarcopenia, while only one patient had normal muscle mass. The mean age was 56.59?±?11.46?years. When patients were divided into three groups according to IIEF-5 score, 38 had severe ED, 39 had moderate ED, and 21 had no ED. There was a significant difference between the three groups in terms of handgrip strength, timed up and go test scores, upper mid-arm circumference, and calf circumference (p?Conclusions: Although muscle mass remains unchanged, muscle strength and physical performance decrease in diabetic ED patients. Diabetic patients with severe and moderate ED have lower muscle strength and physical performance.     

Asymptotic behaviour of binned kernel density estimators for locally non-stationary random fields

We investigate the asymptotic behaviour of binned kernel density estimators for dependent and locally non-stationary random fields converging to stationary random fields. We focus on the study of the bias and the asymptotic normality of the estimators. A simulation experiment conducted shows that both the kernel density estimator and the binned kernel density estimator have the same behavior and both estimate accurately the true density when the number of fields increases. We apply our results to the 2002 incidence rates of tuberculosis in the departments of France.     

Multivariate forests with missing mixed outcomes

In this article, we propose a multivariate random forest method for multiple responses of mixed types with missing responses. Imputation is performed for each bootstrap sample used to build the individual trees that form the forest. The individual trees are built using a weighted splitting rule allowing downweighting of imputed observations. A simulation study shows the benefits of this approach over complete case analysis when missing responses are missing completely at random and missing at random (MAR). In particular, the gain in prediction accuracy of the proposed method is larger in the MAR case and also increases as the proportion of missing increases.     

MIMCA: multiple imputation for categorical variables with multiple correspondence analysis

We propose a multiple imputation method to deal with incomplete categorical data. This method imputes the missing entries using the principal component method dedicated to categorical data: multiple correspondence analysis (MCA). The uncertainty concerning the parameters of the imputation model is reflected using a non-parametric bootstrap. Multiple imputation using MCA (MIMCA) requires estimating a small number of parameters due to the dimensionality reduction property of MCA. It allows the user to impute a large range of data sets. In particular, a high number of categories per variable, a high number of variables or a small number of individuals are not an issue for MIMCA. Through a simulation study based on real data sets, the method is assessed and compared to the reference methods (multiple imputation using the loglinear model, multiple imputation by logistic regressions) as well to the latest works on the topic (multiple imputation by random forests or by the Dirichlet process mixture of products of multinomial distributions model). The proposed method provides a good point estimate of the parameters of the analysis model considered, such as the coefficients of a main effects logistic regression model, and a reliable estimate of the variability of the estimators. In addition, MIMCA has the great advantage that it is substantially less time consuming on data sets of high dimensions than the other multiple imputation methods.     

Price bias and common practice in option pricing

Generally, the semiclosed-form option pricing formula for complex financial models depends on unobservable factors such as stochastic volatility and jump intensity. A popular practice is to use an estimate of these latent factors to compute the option price. However, in many situations this plug-and-play approximation does not yield the appropriate price. This article examines this bias and quantifies its impacts. We decompose the bias into terms that are related to the bias on the unobservable factors and to the precision of their point estimators. The approximated price is found to be highly biased when only the history of the stock price is used to recover the latent states. This bias is corrected when option prices are added to the sample used to recover the states' best estimate. We also show numerically that such a bias is propagated on calibrated parameters, leading to erroneous values. The Canadian Journal of Statistics 48: 8–35; 2020 © 2019 Statistical Society of Canada     

An Empirical Process View of Inverse Regression

A common approach taken in high‐dimensional regression analysis is sliced inverse regression, which separates the range of the response variable into non‐overlapping regions, called ‘slices’. Asymptotic results are usually shown assuming that the slices are fixed, while in practice, estimators are computed with random slices containing the same number of observations. Based on empirical process theory, we present a unified theoretical framework to study these techniques, and revisit popular inverse regression estimators. Furthermore, we introduce a bootstrap methodology that reproduces the laws of Cramér–von Mises test statistics of interest to model dimension, effects of specified covariates and whether or not a sliced inverse regression estimator is appropriate. Finally, we investigate the accuracy of different bootstrap procedures by means of simulations.