Adaptive Warped Kernel Estimators

In this work, we develop a method of adaptive non‐parametric estimation, based on ‘warped’ kernels. The aim is to estimate a real‐valued function s from a sample of random couples (X,Y). We deal with transformed data (Φ(X),Y), with Φ a one‐to‐one function, to build a collection of kernel estimators. The data‐driven bandwidth selection is performed with a method inspired by Goldenshluger and Lepski (Ann. Statist., 39, 2011, 1608). The method permits to handle various problems such as additive and multiplicative regression, conditional density estimation, hazard rate estimation based on randomly right‐censored data, and cumulative distribution function estimation from current‐status data. The interest is threefold. First, the squared‐bias/variance trade‐off is automatically realized. Next, non‐asymptotic risk bounds are derived. Lastly, the estimator is easily computed, thanks to its simple expression: a short simulation study is presented.     

更富裕是否意味着更幸福?基于横截面时间序列数据的分析(2003-2013)

四十年前,经济学家伊斯特林提出了“幸福感悖论”:短时期内个人和国家的财富水平与幸福感都正向相关,而在长期背景下,经济增长并不会显著提高整体幸福感水平。本文使用横跨十年的全国代表性横截面时间序列数据直接检验了财富与幸福感的关系,结果证实了“幸福感悖论”。研究发现,虽然在短时期内,无论是家庭人均收入还是省份的人均生产总值都与幸福感有显著的正相关,但在经济高速发展的十年内(2003-2013),省份人均生产总值的变化与幸福感的变化之间并没有显著相关。     

Undercounts in Offender Data and Closing the Gap Between Indigenous and Other Australians

Closing the gaps between Indigenous and other Australian outcomes is a central concern of contemporary policy. The Re‐Offending Database (ROD), which has been collated by the New South Wales Bureau of Crime Statistics and Research (BOCSAR), offers a unique opportunity to analyse data quality issues in an important source of administrative data for Indigenous people. This paper provides several independent estimates of the population of Indigenous offenders by estimating the number of people with unknown Indigenous status who are likely to be identified as Indigenous in other circumstances. The main finding is that the Indigenous population of offenders is substantially undercounted in administrative data collections. The failure to account for this will understate Indigenous disadvantage or the ‘gap’ between Indigenous and non‐Indigenous outcomes.     

Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data

The logratio methodology is not applicable when rounded zeros occur in compositional data. There are many methods to deal with rounded zeros. However, some methods are not suitable for analyzing data sets with high dimensionality. Recently, related methods have been developed, but they cannot balance the calculation time and accuracy. For further improvement, we propose a method based on regression imputation with Q-mode clustering. This method forms the groups of parts and builds partial least squares regression with these groups using centered logratio coordinates. We also prove that using centered logratio coordinates or isometric logratio coordinates in the response of partial least squares regression have the equivalent results for the replacement of rounded zeros. Simulation study and real example are conducted to analyze the performance of the proposed method. The results show that the proposed method can reduce the calculation time in higher dimensions and improve the quality of results.     

A flexible semiparametric regression model for bimodal,asymmetric and censored data

In this paper, we propose a new semiparametric heteroscedastic regression model allowing for positive and negative skewness and bimodal shapes using the B-spline basis for nonlinear effects. The proposed distribution is based on the generalized additive models for location, scale and shape framework in order to model any or all parameters of the distribution using parametric linear and/or nonparametric smooth functions of explanatory variables. We motivate the new model by means of Monte Carlo simulations, thus ignoring the skewness and bimodality of the random errors in semiparametric regression models, which may introduce biases on the parameter estimates and/or on the estimation of the associated variability measures. An iterative estimation process and some diagnostic methods are investigated. Applications to two real data sets are presented and the method is compared to the usual regression methods.     

大数据时代的意识形态话语权：挑战及应对

大数据作为一场深刻的社会变革产生了巨大影响,相关关系与混杂性的凸显,海量信息与交换方式的多样化等替代了因果关系,弱化了精确性、客观性以及真理的唯一性,改变了人们的认知模式和交往方式,消解了人们对于主流意识形态的认同。不仅如此,大数据的应用促进了互联网的开放性和各种自媒体的发展,为多元思潮、多元文化和多元意识形态的发展与竞争提供了更大的空间,给我国主流意识形态及其安全带来了很大挑战。认真研究大数据产生的影响,强化意识形态观念,利用大数据及各种新兴媒体与互联网,加强我国主流意识形态建设,以马克思主义意识形态和社会主义核心价值观整合和引领多元思潮、多元文化和多元意识形态,增强主流意识形态的感染力与指导作用,为习近平新时代社会主义事业提供坚强思想基础和政治保障,是一项必须完成的重要任务。     

Comparison of predictions by kriging and spatial autoregressive models

In geostatistics, the prediction of unknown quantities at given locations is commonly made by the kriging technique. In addition to the kriging technique for modeling regular lattice spatial data, the spatial autoregressive models can also be used. In this article, the spatial autoregressive model and the kriging technique are introduced. We extend prediction method proposed by Basu and Reinsel for SAR(2,1) model. Then, using a simulation study and real data, we compare prediction accuracy of the spatial autoregressive models with that of the kriging prediction. The results of simulation study show that predictions made by the autoregressive models are good competitor for the kriging method.     

Estimation of the integrated volatility using noisy high-frequency data with jumps and endogeneity

In this paper, we investigate a new estimator of the integrated volatility of Itô semimartingales in the presence of both market microstructure noise and jumps when sampling times are endogenous. In the first step, our estimation wipes off the effects of the microstructure noise, and in the second step our estimator shrinks the effects of jumps. We provide consistency of the estimator when the jumps have finite variation and infinite variation and establish a central limit theorem for the estimator in a general endogenous time setting when the jumps only have finite variation. Simulation illustrates the performance of the proposed estimator.     

On the three-dimensional negative binomial distribution

We consider a particular generalization of the negative binomial distribution to the multivariate case obtained through a specification of the probability generating function as the negative power of a certain polynomial. The probability function itself has previously been derived for the two-dimensional case only, and inference in the multivariate negative binomial distribution has been restricted to the use of composite likelihood based on one- or two-dimensional marginals. In this article, we derive the three-dimensional probability function as a sum with all terms positive and study the range of possible parameter values. We illustrate the use of the three-dimensional distribution for modeling three correlated SAR images.