Generalized method of moments (GMM) estimation has become an important unifying framework for inference in econometrics in the last 20 years. It can be thought of as encompassing almost all of the common estimation methods, such as maximum likelihood, ordinary least squares, instrumental variables, and two-stage least squares, and nowadays is an important part of all advanced econometrics textb...

The uncertain equivalence method (UE) is a newly proposed technique for elicitation of 1-D utilities in the case of monotonic preferences. Previous publications argue that the rationale behind the introduction of this technique is that UE estimates are not influenced by certainty effect, UE elicits points that well describe the curvature of the utility function, which is somewhat closer to the ...

Understanding urban development and defining the problems are essential in urban planning process. Collection and analysis of data are methods that are used to define urban space and its problems. Information systems and computer models are significant tools for applying these methods. Use of information technologies are key components to urban planning education. The syllabus for the fourth se...

l. Introduction. My original intention was to give a talk primarily on the theory of models as applied to the theory of measurement in the empirical sciences. Conversations with Tarski earlier in the symposium changed my mind. We agreed that it would be desirable to have a survey, even if superficial, of the place of the axiomatic method and the theory of models in the empirical sciences. And s...

Copulas are used to depict dependence among several random variables. Both parametric and non-parametric estimation methods have been studied in the literature. Moreover, profile empirical likelihood methods based on either empirical copula estimation or smoothed copula estimation have been proposed to construct confidence intervals of a copula. In this paper, a jackknife empirical likelihood m...

The paper describes a novel method for empirical algorithm design called database learning, and presents experimental results of applying the strategy to designing algorithms for the problem of constructing a maximum independent set in a given graph.

The performance of a number of image processing methods depends on the output quality of a thresholding process. Typical thresholding methods are based on partitioning pixels in an image into two clusters. In this paper, a new thresholding method is presented. The main contribution of the proposed approach is the application of the empirical mode decomposition (EMD) on detecting an optimal thre...

Data warehouses (DWs) can become inconsistent when some dimensional constraints are not satisfied by the dimension instances. In this paper, we present preliminary results about the effects of the violation of partitioning constraints in homogeneous dimension instances over aggregation queries, and in particular over the summarizability property (SUMM) of the DWs. We are interested in finding w...

The purpose of this paper is to discuss empirical risk minimization when the losses are not necessarily bounded and may have a distribution with heavy tails. In such situations usual empirical averages may fail to provide reliable estimates and empirical risk minimization may provide large excess risk. However, some robust mean estimators proposed in the literature may be used to replace empiri...