Bayesian Interval Estimation for the Difference in TPRs and FPRs of Two Diagnostic Tests with Unverified Negatives

We derive Bayesian interval estimators for the differences in the true positive rates and false positive rates of two dichotomous diagnostic tests applied to the members of two distinct populations. The populations have varying disease prevalences with unverified negatives. We compare the performance of the Bayesian credible interval to the Wald interval using Monte Carlo simulation for a spectrum of different TPRs, FPRs, and sample sizes. For the case of a low TPR and low FPR, we found that a Bayesian credible interval with relatively noninformative priors performed well. We obtain similar interval comparison results for the cases of a high TPR and high FPR, a high TPR and low FPR, and of a high TPR and mixed FPR after incorporating mildly informative priors.     

Increasing the sample size at interim for a two‐sample experiment without Type I error inflation

For the case of a one‐sample experiment with known variance σ²=1, it has been shown that at interim analysis the sample size (SS) may be increased by any arbitrary amount provided: (1) The conditional power (CP) at interim is ?50% and (2) there can be no decision to decrease the SS (stop the trial early). In this paper we verify this result for the case of a two‐sample experiment with proportional SS in the treatment groups and an arbitrary common variance. Numerous authors have presented the formula for the CP at interim for a two‐sample test with equal SS in the treatment groups and an arbitrary common variance, for both the one‐ and two‐sided hypothesis tests. In this paper we derive the corresponding formula for the case of unequal, but proportional SS in the treatment groups for both one‐sided superiority and two‐sided hypothesis tests. Finally, we present an SAS macro for doing this calculation and provide a worked out hypothetical example. In discussion we note that this type of trial design trades the ability to stop early (for lack of efficacy) for the elimination of the Type I error penalty. The loss of early stopping requires that such a design employs a data monitoring committee, blinding of the sponsor to the interim calculations, and pre‐planning of how much and under what conditions to increase the SS and that this all be formally written into an interim analysis plan before the start of the study. Copyright © 2009 John Wiley & Sons, Ltd.     

The concept of risk unbiasedness in statistical prediction

This paper extends the concept of risk unbiasedness for applying to statistical prediction and nonstandard inference problems, by formalizing the idea that a risk unbiased predictor should be at least as close to the “true” predictant as to any “wrong” predictant, on the average. A novel aspect of our approach is measuring closeness between a predicted value and the predictant by a regret function, derived suitably from the given loss function. The general concept is more relevant than mean unbiasedness, especially for asymmetric loss functions. For squared error loss, we present a method for deriving best (minimum risk) risk unbiased predictors when the regression function is linear in a function of the parameters. We derive a Rao–Blackwell type result for a class of loss functions that includes squared error and LINEX losses as special cases. For location-scale families, we prove that if a unique best risk unbiased predictor exists, then it is equivariant. The concepts and results are illustrated with several examples. One interesting finding is that in some problems a best unbiased predictor does not exist, but a best risk unbiased predictor can be obtained. Thus, risk unbiasedness can be a useful tool for selecting a predictor.     

Model weights for model choice and averaging

A method is suggested to estimate posterior model probabilities and model averaged parameters via MCMC sampling under a Bayesian approach. The estimates use pooled output for J models (J>1) whereby all models are updated at each iteration. Posterior probabilities are based on averages of continuous weights obtained for each model at each iteration, while samples of averaged parameters are obtained from iteration specific averages that are based on these weights. Parallel sampling of models assists in deriving posterior densities for parameter contrasts between models and in assessing hypotheses regarding model averaged parameters. Four worked examples illustrate application of the approach, two involving fixed effect regression, and two involving random effects.     

Local likelihood with time-varying additive hazards model

The authors propose the local likelihood method for the time-varying coefficient additive hazards model. They use the Newton-Raphson algorithm to maximize the likelihood into which a local polynomial expansion has been incorporated. They establish the asymptotic properties for the time-varying coefficient estimators and derive explicit expressions for the variance and bias. The authors present simulation results describing the performance of their approach for finite sample sizes. Their numerical comparisons show the stability and efficiency of the local maximum likelihood estimator. They finally illustrate their proposal with data from a laryngeal cancer clinical study.     

论我国保险法上的保险利益观——以损失补偿保险为主

保险利益不能等同于物本身,同一物上可以同时存在多个保险利益,并据此成立多个保险关系而不构成重复保险。在损失补偿保险中,风险损失是保险利益的反面,保险事故发生时的损失者才是保险利益的真正主体,权利和利益的复杂关系决定了物权人不一定是保险利益的主体。因此,我们应放弃形式主义的保险利益观而坚持实质性的保险利益观,即以风险归属作为保险利益的归属依据。     

Nonparametric causal effects based on marginal structural models

A parametric marginal structural model (PMSM) approach to Causal Inference has been favored since the introduction of MSMs by Robins [1998a. Marginal structural models. In: 1997 Proceedings of the American Statistical Association. American Statistical Association, Alexandria, VA, pp. 1–10]. We propose an alternative, nonparametric MSM (NPMSM) approach that extends the definition of causal parameters of interest and causal effects. This approach is appealing in practice as it does not require correct specification of a parametric model but instead relies on a working model which can be willingly misspecified. We propose a methodology for longitudinal data to generate and estimate so-called NPMSM parameters describing so-called nonparametric causal effects and provide insight on how to interpret these parameters causally in practice. Results are illustrated with a point treatment simulation study. The proposed NPMSM approach to Causal Inference is compared to the more typical PMSM approach and we contribute to the general understanding of PMSM estimation by addressing the issue of PMSM misspecification.     

一种快速全局运动估计算法

为了对全局运动进行准确快速的估计,在对全局运动估计算法进行研究的基础上,提出了一种快速全局运动估计算法。该算法基于非线性密度估计,采用了六参数的仿射模型。为了提高计算速度,采用了3层金字塔进行多分辨率计算,而且在每层迭代计算中,将基于块的外点去除算法与特征点提取算法相结合,这样既加快了算法的速度,又提高了计算结果的准确性。全局运动估计的实验结果表明,该算法在速度和准确性上具有一定的优势。     

农民工对中国经济的贡献测算

中国的农民工阶层是改革开放以后才出现的。根据公开的统计数据,近年来,农民工流动的规模每年已经超过1亿人。农民工占城市第二、第三产业劳动力的比重很多地方已经超过50%。可以说,农民工已经成为城市第二、第三产业劳动力大军的重要组成部分,农民工对中国国民经济的贡献功不可没。文章利用农业部农村固定观察点的数据,测算了农民工对全国第二、第三产业产值增长的贡献。研究结果表明,1995年至2005年,如果假设农民工劳动生产率与城市产业工人一样,则农民工对中国第二、第三产业产值贡献占到这些产业产值的14.77%~24.35%,平均每年为19.98%;如果假设农民工劳动生产率低于城市产业工人,则农民工对中国第二、第三产业产值贡献占到这些产业产值的1.67%~4.71%,平均每年为3.26%,而且贡献份额有逐年增长的趋势。     

A Note on Trend Decomposition: The 'Classical' Approach Revisited with an Application to Surface Temperature Trends

This note reconsiders the 'classical' approach to trend estimation and presents a modern treatment of this technique that enables trend filters which incorporate end-effects to be constructed easily and efficiently. The approach is illustrated by estimating recent Northern Hemispheric temperature trends. In so doing, it shows how classical trend models may be selected in empirical applications and indicates how this choice determines the properties of the latest trend estimates.