Reinforcement‐based vs. Belief‐based Learning Models in Experimental Asymmetric‐information Games

This paper examines the abilities of learning models to describe subject behavior in experiments. A new experiment involving multistage asymmetric‐information games is conducted, and the experimental data are compared with the predictions of Nash equilibrium and two types of learning model: a reinforcement‐based model similar to that used by Roth and Erev (1995), and belief‐based models similar to the ‘cautious fictitious play’ of Fudenberg and Levine (1995, 1998) These models make predictions that are qualitatively similar cycling around the Nash equilibrium that is much more apparent than movement toward it. While subject behavior is not adequately described by Nash equilibrium, it is consistent with the qualitative predictions of the learning models. We examine several criteria for quantitatively comparing the predictions of alternative models. According to almost all of these criteria, both types of learning model outperform Nash equilibrium. According to some criteria, the reinforcement‐based model performs better than any version of the belief‐based model; according to others, there exist versions of the belief‐based model that outperform the reinforcement‐based model. The abilities of these models are further tested with respect to the results of other published experiments. The relative performance of the two learning models depends on the experiment, and varies according to which criterion of success is used. Again, both models perform better than equilibrium in most cases.     

Mixture experiments with mixing errors

In mixture experiments the properties of mixtures are usually studied by mixing the amounts of the mixture components that are required to obtain the necessary proportions. This paper considers the impact of inaccuracies in discharging the required amounts of the mixture components on the statistical analysis of the data. It shows how the regression calibration approach can be used to minimize the resulting bias in the model and in the estimates of the model parameters, as well as to find correct estimates of the corresponding variances. Its application is made difficult by the complex structure of these errors. We also show how knowledge of the form of the model bias allows for choosing a manufacturing setting for a mixture product that is not biased and has smaller signal to noise ratio.     

Balanced k-nearest neighbour imputation

Random imputation is an interesting class of imputation methods to handle item nonresponse because it tends to preserve the distribution of the imputed variable. However, such methods amplify the total variance of the estimators because values are imputed at random. This increase in variance is called imputation variance. In this paper, we propose a new random hot-deck imputation method that is based on the k-nearest neighbour methodology. It replaces the missing value of a unit with the observed value of a similar unit. Calibration and balanced sampling are applied to minimize the imputation variance. Moreover, our proposed method provides triple protection against nonresponse bias. This means that if at least one out of three specified models holds, then the resulting total estimator is unbiased. Finally, our approach allows the user to perform consistency edits and to impute simultaneously.     

通道失配无源校正算法的收敛性分析

通道失配是空间谱估计测向体制必须解决的问题。已经提出了两大类校正措施。分析了其中的无源校正算法的收敛性问题,提出了它在Φ域收敛的定理和充要条件定理,并给出了证明。无源校正算法在很多场合下更为可行,并在一般情况下也是可靠的。     

Simulation-Extrapolation via the Bezier Curve in Measurement Error Models

Simulation-extrapolation (SIMEX) is a method for correcting for bias in measurement error models, and parametric SIMEX estimates are often used. In this paper, we propose a nonparametric method for computing the SIMEX estimate via the Bezier curve, which is a popular smoothing technique in the computer graphics area. Comparisons are done for the bias of the limit values of parametric SIMEX estimates and the Bezier estimate in the various nonlinear measurement error models.     

RCS动态测量雷达幅度相位校准技术

某相参雷达散射截面积(RCS)动态测量雷达可采用脉冲压缩技术,其接收机信号输出幅度、相位校准需要有标准信号源,目前国内能够买到的国外综合信号源,远不能满足校准要求.文中采用等效自校准原理,研制了RCS动态测量雷达幅度、相位校准系统,解决了采用脉冲压缩技术的RCS动态测量雷达的幅度、相位校准问题.     

Bayesian Modeling of Measurement Errors and Pesticide Concentration in Dietary Risk Assessments

We propose new models for dealing with various sources of variability and uncertainty that influence risk assessments for dietary exposure. The uncertain or random variables involved can interact in complex ways, and the focus is on methodology for integrating their effects and on assessing the relative importance of including different uncertainty model components in the calculation of dietary exposures to contaminants, such as pesticide residues. The combined effect is reflected in the final inferences about the population of residues and subsequent exposure assessments. In particular, we show how measurement uncertainty can have a significant impact on results and discuss novel statistical options for modeling this uncertainty. The effect of measurement error is often ignored, perhaps due to the laboratory process conforming to the relevant international standards, for example, or is treated in an  ad hoc  way. These issues are common to many dietary risk analysis problems, and the methods could be applied to any food and chemical of interest. An example is presented using data on carbendazim in apples and consumption surveys of toddlers.     

A Note on the Aggregation of Event Probabilities

In this article, multiple forecasts given as probabilities of events are aggregated using two assumptions: calibration and conditional independence. The forecasts are treated as data and the aggregation is based on Bayes's theorem. A measure of discrimination is given and the behavior of the aggregated posterior probability is examined as the number of forecasters grows without bound. The work is motivated by recent research efforts employing large numbers of individual forecasts.     

Simex approaches to measurement error in roc studies

This paper explores the estimation of the area under the ROC curve when test scores are subject to errors. The naive approach that ignores measurement errors generally yields inconsistent estimates. Finding the asymptotic bias of the naive estimator, Coffin and Sukhatme (1995, 1997) proposed bias-corrected estimators for parametric and nonparametric cases. However, the asymptotic distributions of these estimators have not been developed because of their complexity. We propose several alternative approaches, including the SIMEX procedure of Cook and Stefanski (1994). We also provide the asymptotic distributions of the SIMEX estimators for use in statistical inference. Small simulation studies illustrate that the SIMEX estimators perform reasonably well when compared to the bias-corrected estimators.     

Expected estimating equations to accommodate covariate measurement error

Estimating equations which are not necessarily likelihood-based score equations are becoming increasingly popular for estimating regression model parameters. This paper is concerned with estimation based on general estimating equations when true covariate data are missing for all the study subjects, but surrogate or mismeasured covariates are available instead. The method is motivated by the covariate measurement error problem in marginal or partly conditional regression of longitudinal data. We propose to base estimation on the expectation of the complete data estimating equation conditioned on available data. The regression parameters and other nuisance parameters are estimated simultaneously by solving the resulting estimating equations. The expected estimating equation (EEE) estimator is equal to the maximum likelihood estimator if the complete data scores are likelihood scores and conditioning is with respect to all the available data. A pseudo-EEE estimator, which requires less computation, is also investigated. Asymptotic distribution theory is derived. Small sample simulations are conducted when the error process is an order 1 autoregressive model. Regression calibration is extended to this setting and compared with the EEE approach. We demonstrate the methods on data from a longitudinal study of the relationship between childhood growth and adult obesity.