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.     

Wavelet Shrinkage with Double Weibull Prior

In this article, we propose a denoising methodology in the wavelet domain based on a Bayesian hierarchical model using Double Weibull prior. We propose two estimators, one based on posterior mean (Double Weibull Wavelet Shrinker, DWWS) and the other based on larger posterior mode (DWWS-LPM), and show how to calculate them efficiently. Traditionally, mixture priors have been used for modeling sparse wavelet coefficients. The interesting feature of this article is the use of non-mixture prior. We show that the methodology provides good denoising performance, comparable even to state-of-the-art methods that use mixture priors and empirical Bayes setting of hyperparameters, which is demonstrated by extensive simulations on standardly used test functions. An application to real-word dataset is also considered.     

A VARYING-COEFFICIENT APPROACH TO ESTIMATION AND EXTRAPOLATION OF HOUSEHOLD SIZE

Household formation analysis is both a multidimensional economical and statistical problem of great complexity. Since most of the literature tries to incorporate multiple economic aspects, there is, considering the extraordinary practical relevance of the problem, a remarkable gap between theory and application in this field. This paper tries to diminish this gap by a comprehensive treatise on the statistical site of the problem. Thus, we develop a model of household composition, where the evolution of the household membership rates is captured by a logit link-function and a multinomial distribution, which automatically fulfills the non-negativity and adding-up restrictions of the underlying probabilities. We use a varying-coefficients procedure by polynomially smoothing the household membership rates over age for every household size class and assuming a linear predictor in other variables. As an application we estimated and extrapolated the distribution of household sizes of an autonomous region using population register data. Our sample consisted of approximately 450,000 people living in about 170,000 households, grouped into nine different household size classes and classified into age classes from 0 to 90. The data covers a time span of 12 years, from 1986 to 1997. Empirical results show the robustness of the procedure even in case of low cell frequencies. Thus, there is no need for regional or age-group aggregations.     

Psychosocial risk factors for excessive gestational weight gain: A systematic review

BackgroundExcessive weight gain during pregnancy can have adverse health outcomes for mother and infant throughout pregnancy. However, few studies have identified the psychosocial factors that contribute to women gaining excessive weight during pregnancy.AimTo review the existing literature that explores the impact of psychosocial risk factors (psychological distress, body image dissatisfaction, social support, self-efficacy and self-esteem) on excessive gestational weight gain.MethodsA systematic review of peer-reviewed English articles using Academic Search Complete, Cumulative Index to Nursing and Allied Health Literature, MEDLINE Complete, PsycINFO, Informit, Web of Science, and Scopus was conducted. Quantitative studies that investigated psychosocial factors of excessive GWG, published between 2000 and 2014 were included. Studies investigating mothers with a low risk of mental health issues and normally-developing foetuses were eligible for inclusion. From the total of 474 articles located, 12 articles were identified as relevant and were subsequently reviewed in full.FindingsSignificant associations were found between depression, body image dissatisfaction, and social support with excessive gestational weight gain. No significant relationships were reported between anxiety, stress, self-efficacy, or self-esteem and excessive gestational weight gain.ConclusionThe relationship between psychosocial factors and weight gain in pregnancy is complex; however depression, body dissatisfaction and social support appear to have a direct relationship with excessive gestational weight gain. Further research is needed to identify how screening for, and responding to, psychosocial risk factors for excessive gestational weight gain can be successfully incorporated into current antenatal care.     

客观评价教学实习成绩

本文不仅从“教”方而且从“学”方视角,对教学实习成绩进行多级综合评价量化处理,建立一整套客观评定教学实习成绩系统。     

Rare and clustered population estimation using the adaptive cluster sampling with some robust measures

The use of robust measures helps to increase the precision of the estimators, especially for the estimation of extremely skewed distributions. In this article, a generalized ratio estimator is proposed by using some robust measures with single auxiliary variable under the adaptive cluster sampling (ACS) design. We have incorporated tri-mean (TM), mid-range (MR) and Hodges-Lehman (HL) of the auxiliary variable as robust measures together with some conventional measures. The expressions of bias and mean square error (MSE) of the proposed generalized ratio estimator are derived. Two types of numerical study have been conducted using artificial clustered population and real data application to examine the performance of the proposed estimator over the usual mean per unit estimator under simple random sampling (SRS). Related results of the simulation study show that the proposed estimators provide better estimation results on both real and artificial population over the competing estimators.     

Bayesian estimation of reliability using time-to-failure distribution of parametric degradation models

This paper focusses on computing the Bayesian reliability of components whose performance characteristics (degradation – fatigue and cracks) are observed during a specified period of time. Depending upon the nature of degradation data collected, we fit a monotone increasing or decreasing function for the data. Since the components are supposed to have different lifetimes, the rate of degradation is assumed to be a random variable. At a critical level of degradation, the time to failure distribution is obtained. The exponential and power degradation models are studied and exponential density function is assumed for the random variable representing the rate of degradation. The maximum likelihood estimator and Bayesian estimator of the parameter of exponential density function, predictive distribution, hierarchical Bayes approach and robustness of the posterior mean are presented. The Gibbs sampling algorithm is used to obtain the Bayesian estimates of the parameter. Illustrations are provided for the train wheel degradation data.     

A seasonal mixed-POT model to estimate high flood quantiles from different event types and seasons

Flood events can be caused by several different meteorological circumstances. For example, heavy rain events often lead to short flood events with high peaks, whereas snowmelt normally results in events of very long duration with a high volume. Both event types have to be considered in the design of flood protection systems. Unfortunately, all these different event types are often included in annual maximum series (AMS) leading to inhomogeneous samples. Moreover, certain event types are underrepresented in the AMS. This is especially unsatisfactory if the most extreme events result from such an event type. Therefore, monthly maximum data are used to enlarge the information spectrum on the different event types. Of course, not all events can be included in the flood statistics because not every monthly maximum can be declared as a flood. To take this into account, a mixture Peak-over-threshold model is applied, with thresholds specifying flood events of several types that occur in a season of the year. This model is then extended to cover the seasonal type of the data. The applicability is shown in a German case study, where the impact of the single event types in different parts of a year is evaluated.     

Composite Estimation for Single‐Index Models with Responses Subject to Detection Limits

We propose a semiparametric estimator for single‐index models with censored responses due to detection limits. In the presence of left censoring, the mean function cannot be identified without any parametric distributional assumptions, but the quantile function is still identifiable at upper quantile levels. To avoid parametric distributional assumption, we propose to fit censored quantile regression and combine information across quantile levels to estimate the unknown smooth link function and the index parameter. Under some regularity conditions, we show that the estimated link function achieves the non‐parametric optimal convergence rate, and the estimated index parameter is asymptotically normal. The simulation study shows that the proposed estimator is competitive with the omniscient least squares estimator based on the latent uncensored responses for data with normal errors but much more efficient for heavy‐tailed data under light and moderate censoring. The practical value of the proposed method is demonstrated through the analysis of a human immunodeficiency virus antibody data set.