On Asymptotic Normality of the Local Polynomial Regression Estimator with Stochastic Bandwidths

Nonparametric density and regression estimators commonly depend on a bandwidth. The asymptotic properties of these estimators have been widely studied when bandwidths are non stochastic. In practice, however, in order to improve finite sample performance of these estimators, bandwidths are selected by data driven methods, such as cross-validation or plug-in procedures. As a result, nonparametric estimators are usually constructed using stochastic bandwidths. In this article, we establish the asymptotic equivalence in probability of local polynomial regression estimators under stochastic and nonstochastic bandwidths. Our result extends previous work by Boente and Fraiman (1995 Boente , G. , Fraiman , R. ( 1995 ). Asymptotic distribution of data-driven smoothers in density and regression estimation under dependence . Can. J. Statist. 23 : 383 – 397 .[Crossref], [Web of Science ®] , [Google Scholar]) and Ziegler (2004 Ziegler , K. ( 2004 ). Adaptive kernel estimation of the mode in nonparametric random design regression model . Probab. Mathemat. Statist. 24 : 213 – 235 . [Google Scholar]).     

Modeling Interval Time Series with Space–Time Processes

We consider interval-valued time series, that is, series resulting from collecting real intervals as an ordered sequence through time. Since the lower and upper bounds of the observed intervals at each time point are in fact values of the same variable, they are naturally related. We propose modeling interval time series with space–time autoregressive models and, based on the process appropriate for the interval bounds, we derive the model for the intervals’ center and radius. A simulation study and an application with data of daily wind speed at different meteorological stations in Ireland illustrate that the proposed approach is appropriate and useful.     

The performance of the truncated mixed control chart

Abstract

When the mixed chart proposed by Aslam et al. (2015 Aslam, M., M. Azam, N. Khan, and C.-H. Jun. 2015. A mixed control chart to monitor the process. International Journal of Production Research 53 (15):4684–93. doi:10.1080/00207543.2015.1031354.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) is in use, the sample items are classified as defective or not defective and, depending on the number of defectives, the quality characteristic X of the sample items are also measured. In this case, an Xbar chart decides the state of the process. The previous conforming/non-conforming classification truncates the X distribution and, because of that, the mathematical development to obtain the ARLs is complex. Aslam et al. (2015 Aslam, M., M. Azam, N. Khan, and C.-H. Jun. 2015. A mixed control chart to monitor the process. International Journal of Production Research 53 (15):4684–93. doi:10.1080/00207543.2015.1031354.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) didn’t pay attention to the fact that the X distribution is truncated and, due to that, they obtained incorrect ARLs.     

The effect of temporal aggregation on the estimation accuracy of ARMA models

ABSTRACT

Autoregressive Moving Average (ARMA) time series model fitting is a procedure often based on aggregate data, where parameter estimation plays a key role. Therefore, we analyze the effect of temporal aggregation on the accuracy of parameter estimation of mixed ARMA and MA models. We derive the expressions required to compute the parameter values of the aggregate models as functions of the basic model parameters in order to compare their estimation accuracy. To this end, a simulation experiment shows that aggregation causes a severe accuracy loss that increases with the order of aggregation, leading to poor accuracy.     

Subjective well-being and school engagement before versus during the COVID-19 pandemic: What good are positive emotions?

The present study overcomes the limited empirical evidence on the association between well-being and school engagement in times of adversity by exploiting available data from two large and comparable samples of eighth graders; one obtained prior to the COVID-19 pandemic and the second obtained during the pandemic. Results suggest that adolescents were less engaged with their learning context during the pandemic, as well as lower in positive and negative affect, but slightly more satisfied with life. Through SEM we found a stronger positive association between positive affect and school engagement in the COVID-19 group compared with the pre-COVID-19 group. This finding highlights the important role of positive affect in supporting better academic functioning in the aftermath of a global crisis.     

Bayesian inference for a flexible class of bivariate beta distributions

Several bivariate beta distributions have been proposed in the literature. In particular, Olkin and Liu [A bivariate beta distribution. Statist Probab Lett. 2003;62(4):407–412] proposed a 3 parameter bivariate beta model which Arnold and Ng [Flexible bivariate beta distributions. J Multivariate Anal. 2011;102(8):1194–1202] extend to 5 and 8 parameter models. The 3 parameter model allows for only positive correlation, while the latter models can accommodate both positive and negative correlation. However, these come at the expense of a density that is mathematically intractable. The focus of this research is on Bayesian estimation for the 5 and 8 parameter models. Since the likelihood does not exist in closed form, we apply approximate Bayesian computation, a likelihood free approach. Simulation studies have been carried out for the 5 and 8 parameter cases under various priors and tolerance levels. We apply the 5 parameter model to a real data set by allowing the model to serve as a prior to correlated proportions of a bivariate beta binomial model. Results and comparisons are then discussed.     

The Frisch Elasticity in the Mercosur Countries: A Pseudo‐Panel Approach

In estimating the Frisch elasticity – i.e., the elasticity of substitution between worked hours and real wages, holding constant the marginal utility of wealth – for the Mercosur countries, this article finds a strong heterogeneity. It argues that severe liquidity constraints are the main reason for the negative estimates and that the heterogeneity is the outcome of differences in many relevant economic dimensions – ranging from sectorial specialisation to welfare‐state provisions and labour‐market specificities. The diversity of Frisch elasticities calls for the development of a cross‐country (rather than a within‐country) policy approach, since they crucially affect the dynamics of the business cycle, the synchronisation of which is a step prior to the design of macro‐convergence policies in the Mercosur context.     

“Governance and corruption: is history repeating itself?” Fostering a debate and inviting contributions from a multidisciplinary perspective

Corruption is a critical phenomenon, both domestically and internationally. Corruption is a concept difficult to circumscribe referring to a large variety of levels, expressions and profiles. Due to its characteristics corruption has to be approached assuming a multi-perspective view. We believe that corruption ha to be studies assuming that this is multi-perspective and multi-disciplinary. We support the idea that governance perspective has to be considered central and innovative. The multi-disciplinary perspective wasn't completely explored in order to comprehend how the corruption phenomenon works and which consequences it is able to determine. Our study aims to stimulate the debate on the link between governance and the phenomenon of corruption as an illness affecting our capitalistic societies in many respects.     

Markov random field-based image subsampling method

The use of Bayesian models for the reconstruction of images degraded by both some blurring function H and the presence of noise has become popular in recent years. Making an analogy between classical degradation processes and resampling, we propose a Bayesian model for generating finer resolution images. The approach involves defining resampling, or aggregation, as a linear operator applied to an original picture to produce derived lower resolution data which represent our available experimental infor-mation. Within this framework, the operation of making inference on the orginal data can be viewed as an inverse linear transformation problem. This problem, formalized through Bayes' theorem, can be solved by the classical maximum a posteriori estimation procedure. Image local characteristics are assumed to follow a Gaussian Markov random field. Under some mild assumptions, simple, iterative and local operations are involved, making parallel 'relaxation' processing feasible. experimental results are shown on some images, for which good subsampling estimates are obtained.