The Semiparametric Least Squares (SLS) estimation for single index models is studied. Applying the isometric regression by Ayer et al (1955), the method minimizes the mean squared errors with respect to both finite and infinite dimensional parameters. A proof of consistency and an upper bound of convergence rates is offered. As an application example of the SLS estimation, asymptotic normality ...

This chapter proposes a combination of estimation of distribution algorithm (EDA) and the 2-opt local search algorithm (EDA/LS) for the quadratic assignment problem (QAP). In EDA/LS, a new operator, called guided mutation, is employed for generating new solutions. This operator uses both global statistical information collected from the previous search and the location information of solutions ...

Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata programs for this purpose: -mdrawsfor deriving draws from the standard uniform density using either Halton or pseudo-random sequences, and an egen function -mvnp()for calc...

Timing errors that are caused by the timing violations of sensitized circuit paths, have emerged as an important threat to the reliability of synchronous digital circuits. To protect circuits from these timing errors, designers typically use a conservative timing margin, which leads to operational inefficiency. Existing adaptive approaches reduce such conservative margins by predicting the timi...

System of panel models are popular models in applied sciences and the question of spatial errors has created the recent demand for spatial system estimation of panel models. Therefore we propose new diagnostic methods to explore if the spatial component will change significantly the outcome of non-spatial estimates of seemingly unrelated regression (SUR) systems. We apply a local sensitivity ap...

A least squares estimation approach for the estimation of a GARCH (1,1) model is developed. The asymptotic properties of the estimator are studied given mild regularity conditions, which require only that the error term has a conditional moment of some order. We establish the consistency, asymptotic normality and the law of iterated logarithm for our estimate. The finite sample properties are a...

Johansen’s reduced rank maximum likelihood (ML) estimator for cointegration parameters in vector error correction models is known to produce occasional extreme outliers. Using a small monetary system and German data we illustrate the practical importance of this problem. We also consider an alternative generalized least squares (GLS) system estimator which has better properties in this respect....

Consider a non-standard parametric estimation problem, such as the estimation of the AR(1) coefficient close to the unit root. We develop a numerical algorithm that determines an estimator that is nearly (mean or median) unbiased, and among all such estimators, comes close to minimizing a weighted average risk criterion. We demonstrate the usefulness of our generic approach by also applying it ...

A semi parametric profil~ likelihood method is proposed for estimation of sample selection models. The method is a two step scoring semi parametric estimation procedure based on index formulation and kernel density estimation. Under some regularity conditions, the estimator is asymptotically normal. This method can be applied to estimation of general sample selection models with multiple regime...

This work considers the problem of both supervised and unsupervised classification for vector data of mixed types. An important subclass of graphical modeling techniques called Generalized Linear Statistics (GLS) is used to capture the underlying statistical structure of these complex data. The GLS methodology exploits the split between data space and natural parameter space for exponential fam...