Reflections on Accuracy

The difference between test accuracy and predictive accuracy is presented and defined. The failure to distinguish between these two types of measures is shown to have led to a misguided debate over the interpretation of prevalence estimates. The distinction between test accuracy defined as sensitivity and specificity, and predictive accuracy defined as positive and negative predictive value is shown to reflect the choice of the denominator used to calculate true positive, false positive, false negative, and true negative rates. It is further shown that any instrument will tend to overestimate prevalence in low base rate populations and underestimate it in those populations where prevalence is high. The implications of these observations are then discussed in terms of the need to define diagnostic thresholds that have clinical and policy relevance.     

Statistical models for e-learning data

In this paper we analyse a real e-learning dataset derived from the e-learning platform of the University of Pavia. The dataset concerns an online learning environment with in-depth teaching materials. The main focus of this paper is to supply a measure of the relative importance of the exercises (test) at the end of each training unit; to build predictive models of student’s performance and finally to personalize the e-learning platform. The methodology employed is based on nonparametric statistical methods for kernel density estimation and generalized linear models and generalized additive models for predictive purposes.     

Forecasting with univariate TAR models

The forecasting stage in the analysis of a univariate threshold-autoregressive model, with exogenous threshold variable, has been developed in this paper via the computation of the so-called predictive distributions. The procedure permits one to forecast simultaneously the response and exogenous variables. An important issue in this work is the treatment of eventual missing observations present in the two time series before obtaining forecasts.     

Information criteria for the predictive evaluation of bayesian models

As a natural successor of the information criteria AIC and ABIC, information criteria for the Bayes models were developed by evaluating the bias of the log likelihood of the predictive distribution as an estimate of its expected log-likelihood. Considering two specific situations for the true distribution, two information criteria, PIC₁ and PIC₂ are derived. Linear Gaussian cases are considered in details and the evaluation of the maximum a posteriori estimator is also considered. By a simple example of estimating the signal to noise ratio, it was shown that the PIC₂ is a good approximation to the expected log-likelihood in the entire region of the signal to noise ratio. On the other hand, PIC₁ performs good only for the smaller values of the variance ratio. For illustration, the problems of trend estimation and seasonal adjustment are considered. Examples show that the hyper-parameters estimated by the new criteria are usually closer to the best ones than those by the ABIC.     

ON BAYES PREDICTION OF FUTURE MEDIAN

An expression for the Bayesian predictive survival function of the median of a set of future observations is obtained whether its size is assumed to be odd or even. Both of the informative and future samples are drawn from a population whose distribution is a general class that includes several distributions used in life testing (and other areas as well) such as the Weibull (including the exponential and Rayleigh), compound Weibull (including the compound exponential and compound Rayleigh), Pareto, beta, Gompertz and compound Gompertz, among other distributions. A general proper (conjugate) prior density function is used to cover most prior distributions that have been used in literature. Applications to the Weibull, exponential and Rayleigh models are illustrated.     

ON THE EVALUATION OF CONTROL CHART LIMITS BASED ON PREDICTIVE DISTRIBUTIONS

ABSTRACT

We propose a Bayesian approach to obtaining control charts when there is parameter uncertainty. Our approach consists of two stages, (i) construction of the control chart where we use a predictive distribution based on a Bayesian approach to derive the rejection region, and (ii) evaluation of the control chart where we use a sampling theory approach to examine the performance of the control chart under various hypothetical specifications for the data generation model.     

R2 Bounds for Predictive Models: What Univariate Properties Tell us About Multivariate Predictability

ABSTRACT

A long-standing puzzle in macroeconomic forecasting has been that a wide variety of multivariate models have struggled to out-predict univariate models consistently. We seek an explanation for this puzzle in terms of population properties. We derive bounds for the predictive R² of the true, but unknown, multivariate model from univariate ARMA parameters alone. These bounds can be quite tight, implying little forecasting gain even if we knew the true multivariate model. We illustrate using CPI inflation data. Supplementary materials for this article are available online.     

In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?

Abstract

It is widely known that significant in-sample evidence of predictability does not guarantee significant out-of-sample predictability. This is often interpreted as an indication that in-sample evidence is likely to be spurious and should be discounted. In this paper, we question this interpretation. Our analysis shows that neither data mining nor dynamic misspecification of the model under the null nor unmodelled structural change under the null are plausible explanations of the observed tendency of in-sample tests to reject the no-predictability null more often than out-of-sample tests. We provide an alternative explanation based on the higher power of in-sample tests of predictability in many situations. We conclude that results of in-sample tests of predictability will typically be more credible than results of out-of-sample tests.     

Identification,Estimation, and Control of Uncertain Dynamic Systems: A Nonparametric Approach

This article is devoted to a presentation of the author' practice of the non-parametric estimation theory for the estimation, filtering, and control of uncertain dynamic systems. The fundamental advantage of this approach is a weak dependency on prior modeling assumptions about uncertain dynamic components. This approach appears to be of great interest for the control of general discrete-time processes, and in particular, biotechnological processes, which are emblematic of nonlinear uncertain and partially observed systems.     

两类企业公开信息及其交互作用对消费者品牌关系的影响

本文探讨了当反映企业社会责任感的公开信息和反映企业能力的公开信息同时出现时,两种信息会以怎样的交互模式、通过怎样的机制共同影响消费者与品牌的关系强度.以快餐行业公司为对象的实验研究结果显示,当控制已有关系强度后,反映企业社会责任感的公开信息会对消费者-品牌关系强度产生显著的影响,而反映企业能力的公开信息对消费者对品牌的感知,评价以及与消费者-品牌的关系强度都没有显著的影响.而且,两种企业公开信息对消费者-品牌关系强度的影响存在显著的交互作用,交互形式与公平启发理论的预测相符.此外,品牌信任和品牌情感在企业公开信息对关系强度的影响中起到中介作用.