Conditional Density Estimation in the Single Functional Index Model for α-Mixing Functional Data

In this article, we investigate the nonparametric estimation of the conditional density of a scalar response variable Y, given the explanatory variable X taking value in a Hilbert space when the observations are linked with a single index structure. The goal of this article is to present the asymptotic results such as pointwise almost complete consistency and the uniform almost complete convergence of the kernel estimation with rate for the conditional density in the setting of the α-mixing functional data, which extend the i.i.d case in Attaoui et al. (2011 Attaoui , S. , Laksaci , A. , Ould-Said , E. ( 2011 ). A note on the conditional density estimate in the single functional index model . Statist. Probab. Lett. 81 ( 1 ): 45 – 53 .[Crossref], [Web of Science ®] , [Google Scholar]) to the dependence setting. As an application, the convergence rate of the kernel estimation for the conditional mode is also obtained.     

Estimation of the parameters of a general student’s t distribution

We have observations for a t distribution with unknown mean, variance, and degrees of freedom, each of which we wish to estimate. The major problem lies in the estimate of the degrees of freedom. We show that a relatively efficient yet very simple estimator is a given function of the ratio of percentile estimates. We derive the appropriate estimator, provide equations for transformation and standard errors, contrast this with other estimators, and give examples.     

A Note on Estimation of a Distribution Function in a Nonparametric Set-up Using Stein’s Shrinkage Estimation Technique

Based on Stein’s famous shrinkage estimation of a multivariate normal distribution, we propose a new type of estimators of the distribution function of a random variable in a nonparametric setup. The proposed estimators are then compared with the empirical distribution function, which is the best equivariant estimator under a well-known loss function. Our extensive simulation study shows that our proposed estimators can perform better for moderate to large sample sizes.     

What Level of Statistical Model Should We Use in Small Area Estimation?

If unit‐level data are available, small area estimation (SAE) is usually based on models formulated at the unit level, but they are ultimately used to produce estimates at the area level and thus involve area‐level inferences. This paper investigates the circumstances under which using an area‐level model may be more effective. Linear mixed models (LMMs) fitted using different levels of data are applied in SAE to calculate synthetic estimators and empirical best linear unbiased predictors (EBLUPs). The performance of area‐level models is compared with unit‐level models when both individual and aggregate data are available. A key factor is whether there are substantial contextual effects. Ignoring these effects in unit‐level working models can cause biased estimates of regression parameters. The contextual effects can be automatically accounted for in the area‐level models. Using synthetic and EBLUP techniques, small area estimates based on different levels of LMMs are investigated in this paper by means of a simulation study.     

An Efficient Method of Parameter and Quantile Estimation for the Three-Parameter Weibull Distribution Based on Statistics Invariant to Unknown Location Parameter

The three-parameter Weibull distribution is widely used in life testing and reliability analysis. In this article, we propose an efficient method for the estimation of parameters and quantiles of the three-parameter Weibull distribution, which avoids the problem of unbounded likelihood, by using statistics invariant to unknown location. Through a Monte Carlo simulation study, we show that the proposed method performs well compared to other prominent methods based on bias and MSE. Finally, we present two illustrative examples.     

An Example Of The Use Of Andrews’ Plots To Detect Time Variations In Model Parameters And To Detect Outlying Observations

Andrews (1972) introduced a method of plotting highdimensional data in two dimensions. This method is exploited as a graphical tool for the examination of changes over time in the parameters of a time series model. An example using a Fourier series model is given to illustrate the method. It is also shown how outlying observations in the data can be found.

AMS (MOS) Subject Classifivations: 62M10, 62H30     

Estimation of a change point in the variance function based on the χ2-distribution

ABSTRACT

Let us consider that the variance function or its νth derivative in a regression model has a change/discontinuity point at an unknown location. To use the local polynomial fits, the log-variance function which break the positivity is targeted. The location and the jump size of the change point are estimated based on a one-sided kernel-weighted local-likelihood function which is provided by the χ²-distribution. The whole structure of the log-variance function is then estimated using the data sets split by the estimated location. Asymptotic results of the proposed estimators are described. Numerical works demonstrate the performances of the methods with simulated and real examples.     

Estimation of reliability in multicomponent stress–strength based on two parameter exponentiated Weibull Distribution

In this research article, we estimate the multicomponent stress–strength reliability of a system when strength and stress variates are drawn from an exponentiated Weibull distribution with different shape parameters α?and?β, and common shape and scale parameters γ and λ, respectively. We estimate the parameters by using maximum likelihood estimation (MLE) and hence the estimate of reliability obtained applying the MLE method of estimation when samples are drawn from stress and strength distributions. The small sample comparison of the reliability estimates is made through Monte Carlo simulation.     

The efficiency of Efron's “Bootstrap” Approach Applied to Error Rate Estimation in Discriminant Analysis

The “bootstrap” approach of Efron is considered in its application to the estimation of error rates in discriminant analysis. Its efficiency relative to parametric estimation is investigated by simulation for Fisher's linear discriminant function in the context of two multivariate normal populations with a common covariance matrix.     

Authors’ responses to the discussion on “Predicting losses of residential structures in the state of Florida by the Public Hurricane Loss Evaluation Model”

Hurricanes threaten the Florida coast line every year and are capable of causing catastrophic losses. The Public Hurricane Loss Model was developed in response to the need for having an open transparent model to predict the above losses. The results were summarized in the paper “Predicting Losses of Residential Structures in the State of Florida by the Public Hurricane Loss Model” which was subsequently a subject of discussion by experts in Meteorology, Engineering, Actuarial Sciences and Statistics. The present paper presents a response to the discussions on the above mentioned article.     

The Potential Impact of ‘Public Access’ Legislation on Access to Forestry Literature

There have been increasing calls for the United States (U.S.) government’s implementation of broad public access policies mandating free online access to federally funded research. This study examines the potential impact of such a policy on peer-reviewed forestry literature. The authors analyze information about federal government authorship, federal government funding, and U.S. authorship indicated in articles published in five core forestry journals in 2006. The results of the analysis provide evidence that federal public access legislation would have a significant impact on the accessibility of forestry literature published in leading journals in the field.     

A Note on Application of Nesterov’s Method in Solving Lasso-Type Problems

Many different algorithms have been proposed to solve penalized variable selection problems, in particular lasso and its variants, including group lasso and fused lasso. Loss functions other than quadratic loss also pose significant challenges for finding efficient solvers. Here, we note that Nesterov’s method can be used to transform an optimization problem with general smooth convex loss to quadratic loss with identity covariate matrix in each iteration. After such reduction, the problem becomes much easier to solve or even can be solved in closed form in some cases. We perform some simulations and apply our implementation to phoneme discrimination.     

Modelling and research on manufacturer’s capacity of remanufacturing and recycling in closed-loop supply chain

This paper introduces and analyses the present situation of the electronic waste recycling at home and abroad. Involving manufacturers in the recycling process of supply chain as the dominant party and establishing the closed-loop supply chain system structure model of the waste electronic products recycling, the effects of remanufacturing capacity and recycling coefficient on the closed-loop supply chain recycling network are analysed by using system dynamics method. The simulation results show that, compared with the traditional supply chain, the total revenue of the supply chain under the recycling mode dominated by manufacturers has improved, that the increase of the remanufacturing capacity has promoted the growth of the electronic product's sales rate, which reaches a equilibrium when remanufacturing capacity is set as 0.36, and that the recycling efficiency of e-waste has improved significantly as the e-waste recycling coefficient constantly increased.     

Authors’ response: on the role of data,statistics and decisions in a pandemic

AStA Advances in Statistical Analysis -     

Discussion of ‘An analysis of global warming in the Alpine region based on nonlinear nonstationary time series models’ by Battaglia and Protopapas

This discussion focuses on threshold nonstationary?Cnonlinear time series modelling; it raises various issues to do with identifiability and model complexity. It also gives some background history concerning smooth threshold/transition autoregressive models and hidden Markov switching models.     

Super Searchers Go to the Source: The Interviewing and Hands-on Information Strategies of Top Primary Researchers—Online,on the Phone,and in Person

Abstract

OCLC recently announced a plan to implement MARC 21 Format for Holdings Data (MFHD) and invited holdings experts Frieda Rosenberg and Diane Hillmann to serve as advisors and to aid in the implementation process. In December 2004, Jian Wang interviewed Rosenberg and Hillmann. They discuss their longtime involvement with the holdings standard and provide interesting perspectives on the issues, challenges, and benefits for the constituencies (libraries, the serials community, system vendors, and bibliographic utilities) involved with and responsible for implementing and using MFHD.