Cross-validating fit and predictive accuracy of nonlinear quantile regressions

The paper proposes a cross-validation method to address the question of specification search in a multiple nonlinear quantile regression framework. Linear parametric, spline-based partially linear and kernel-based fully nonparametric specifications are contrasted as competitors using cross-validated weighted L ₁-norm based goodness-of-fit and prediction error criteria. The aim is to provide a fair comparison with respect to estimation accuracy and/or predictive ability for different semi- and nonparametric specification paradigms. This is challenging as the model dimension cannot be estimated for all competitors and the meta-parameters such as kernel bandwidths, spline knot numbers and polynomial degrees are difficult to compare. General issues of specification comparability and automated data-driven meta-parameter selection are discussed. The proposed method further allows us to assess the balance between fit and model complexity. An extensive Monte Carlo study and an application to a well-known data set provide empirical illustration of the method.     

Monetary policy in the New-Keynesian model: An application to the Euro Area

This paper analyzes monetary policy in a stylized New-Keynesian model. A number of issues are focused upon: (i) optimal monetary policy under commitment or discretion versus ad-hoc monetary policy based on simple rules, (ii) the effects of fiscal policies and foreign variables on monetary policy, (iii) the effects of fiscal deficit and interest rate smoothing objectives and the role of forward-backward linkages in the model. The model is estimated for the Euro Area. Using simulations of the estimated model, it is analyzed how these aspects might affect monetary policy of the ECB and macroeconomic fluctuations in the Euro Area.     

Correlation of Quantitative Risk Results for High Hazard Processes

A quantitative risk analysis was conducted to evaluate the design of the VX neutralization subsystem and related support facilities of the U.S. Army Newport Chemical Agent Disposal Facility. Three major incidents including agent release, personnel injury, and system loss were studied using fault tree analysis methodology. Each incident was assigned a risk assessment code based on the severity level and probability of occurrence of the incident. Safety mitigations or design changes were recommended to bring the "undesired" risk level (typical agent release events) to be "acceptable with controls" or "acceptable."     

Composite likelihood estimation in multivariate data analysis

The authors propose two composite likelihood estimation procedures for multivariate models with regression/univariate and dependence parameters. One is a two‐stage method based on both univariate and bivariate margins. The other estimates all the parameters simultaneously based on bivariate margins. For some special cases, the authors compare their asymptotic efficiencies with the maximum likelihood method. The performance of the two methods is reasonable, except that the first procedure is inefficient for the regression parameters under strong dependence. The second approach is generally better for the regression parameters, but less efficient for the dependence parameters under weak dependence.     

Efficiency of generalized estimating equations for binary responses

Summary.  Using standard correlation bounds, we show that in generalized estimation equations (GEEs) the so-called 'working correlation matrix' R ( α ) for analysing binary data cannot in general be the true correlation matrix of the data. Methods for estimating the correlation param-eter in current GEE software for binary responses disregard these bounds. To show that the GEE applied on binary data has high efficiency, we use a multivariate binary model so that the covariance matrix from estimating equation theory can be compared with the inverse Fisher information matrix. But R ( α ) should be viewed as the weight matrix, and it should not be confused with the correlation matrix of the binary responses. We also do a comparison with more general weighted estimating equations by using a matrix Cauchy–Schwarz inequality. Our analysis leads to simple rules for the choice of α in an exchangeable or autoregressive AR(1) weight matrix R ( α ), based on the strength of dependence between the binary variables. An example is given to illustrate the assessment of dependence and choice of α .     

一种理解西方人类学发展的历史视角

仔细考察了16世纪和19世纪具体历史事件的相关性.在这些具体的历史事件中,一种对异族异群体的体验产生出了一种人类学的视角.如此一种视角在辩证意叉上是受19世纪中期日益变得清晰的生物学意义上的种族观点所影响的.这样一种文化的视角在争议中渐成支配,并在解释和评论哲学和心理学议题的兼容性方面取得其基础性的地位.