Science Policy Choices and the Estimation of Cancer Risk Associated with Exposure to TCDD

United States regulatory agencies use no-threshold models for estimating carcinogenic risks. Other countries use no-threshold models for carcinogens that are genotoxic and threshold models for carcinogens that are not genotoxic, such as 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin (TCDD or "dioxin"). The U.S. Environmental Protection Agency has proposed a revision of the carcinogenic potency estimate for TCDD that is based on neither a threshold nor a no-threshold model; instead, it is a compromise between risk numbers generated by the two irreconcilably different models. This paper discusses the revision and its implications.     

基于POT-GPD损失分布的农业自然灾害VAR估算

 近年来,干旱、洪涝、雪灾等极端气象灾害频发,强烈冲击农业生产和粮食安全,科学估算农业自然灾害给粮食产出带来的风险价值,对预警农业自然灾害、确保粮食安全具有重要意义。文章针对农业自然灾害大灾损失的低频高损、数据稀少的特点,采用极值理论中的POT 模型对历史观测值超阀值数据进行建模,运用GPD模型对农业自然大灾损失进行拟合,模拟计算农业自然灾害VAR值,为农业自然灾害预警和粮食安全储备提供科学依据。     

Efficient Covariance Estimation for Asynchronous Noisy High‐Frequency Data

Abstract. We focus on estimating the integrated covariance of log‐price processes in the presence of market microstructure noise. We construct a consistent asymptotically unbiased estimator for the quadratic covariation of two Itô processes in the case where high‐frequency asynchronous discrete returns under market microstructure noise are observed. This estimator is based on synchronization and multi‐scale methods and attains the optimal rate of convergence. A lower bound for the rate of convergence is derived from the local asymptotic normality property of the simpler parametric model with equidistant and synchronous observations. A Monte Carlo study analyses the finite sample size characteristics of our estimator.     

我国通货膨胀率非线性特征研究

 本文应用带单位根的门限自回归模型,对我国1990年来以来通货膨胀率的动态路径进行了模拟分析。估计和检验发现了我国通货膨胀具有明显的非线性特征,模型较好地拟合了通货膨胀的动态调整过程。我国通货膨胀调整存在减速通货膨胀状态、适中通货膨胀状态和加速通货膨胀状态三个区制。适中通货膨胀状态是一个平稳的自回归过程,减速通货膨胀状态、加速通货膨胀状态则是具有单位根的自回归过程,具有自我加速的作用。在不同的区制下,通货膨胀率均有较高的持久性,但中间状态的持久性明显低于其他两种状态。     

Small-area estimation in the presence of area-level correlated responses

The Fay-Herriot area-level model for correlated response data is augmented with a between-groups-of-domains effect. Correlated-response parameters of small-area estimates no longer need the assumption of spatial contiguity. A simulation shows that area-level correlated-response observations increase the efficiency of the estimates, but do not reduce the biases.     

Correlations among centrality indices and a class of uniquely ranked graphs

Various centrality indices have been proposed to capture different aspects of structural importance but relations among them are largely unexplained. The most common strategy appears to be the pairwise comparison of centrality indices via correlation. While correlation between centralities is often read as an inherent property of the indices, we argue that it is confounded by network structure in a systematic way. In fact, correlations may be even more indicative of network structure than of relationships between indices. This has substantial implications for the interpretation of centrality effects as it implies that competing explanations embodied in different indices cannot be separated from each other if the network structure is close to a certain generalization of star graphs.     

Scale mixtures log-Birnbaum–Saunders regression models with censored data: a Bayesian approach

The main objective of this paper is to develop a full Bayesian analysis for the Birnbaum–Saunders (BS) regression model based on scale mixtures of the normal (SMN) distribution with right-censored survival data. The BS distributions based on SMN models are a very general approach for analysing lifetime data, which has as special cases the Student-t-BS, slash-BS and the contaminated normal-BS distributions, being a flexible alternative to the use of the corresponding BS distribution or any other well-known compatible model, such as the log-normal distribution. A Gibbs sample algorithm with Metropolis–Hastings algorithm is used to obtain the Bayesian estimates of the parameters. Moreover, some discussions on the model selection to compare the fitted models are given and case-deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback–Leibler divergence. The newly developed procedures are illustrated on a real data set previously analysed under BS regression models.     

Sharp Threshold Detection Based on Sup-Norm Error Rates in High-Dimensional Models

We propose a new estimator, the thresholded scaled Lasso, in high-dimensional threshold regressions. First, we establish an upper bound on the ?_∞ estimation error of the scaled Lasso estimator of Lee, Seo, and Shin. This is a nontrivial task as the literature on high-dimensional models has focused almost exclusively on ?₁ and ?₂ estimation errors. We show that this sup-norm bound can be used to distinguish between zero and nonzero coefficients at a much finer scale than would have been possible using classical oracle inequalities. Thus, our sup-norm bound is tailored to consistent variable selection via thresholding. Our simulations show that thresholding the scaled Lasso yields substantial improvements in terms of variable selection. Finally, we use our estimator to shed further empirical light on the long-running debate on the relationship between the level of debt (public and private) and GDP growth. Supplementary materials for this article are available online.