A Simulation-Based Specification Test for Diffusion Processes

This article makes two contributions. First, we outline a simple simulation-based framework for constructing conditional distributions for multifactor and multidimensional diffusion processes, for the case where the functional form of the conditional density is unknown. The distributions can be used, for example, to form predictive confidence intervals for time period t + τ, given information up to period t. Second, we use the simulation-based approach to construct a test for the correct specification of a diffusion process. The suggested test is in the spirit of the conditional Kolmogorov test of Andrews. However, in the present context the null conditional distribution is unknown and is replaced by its simulated counterpart. The limiting distribution of the test statistic is not nuisance parameter-free. In light of this, asymptotically valid critical values are obtained via appropriate use of the block bootstrap. The suggested test has power against a larger class of alternatives than tests that are constructed using marginal distributions/densities. The findings of a small Monte Carlo experiment underscore the good finite sample properties of the proposed test, and an empirical illustration underscores the ease with which the proposed simulation and testing methodology can be applied.     

Maximum Likelihood Estimation of a Poisson Parameter in a Model of Equioverlapping Samples:A Simulation Study

In a model of equioverlapping samples maximum likelihood estimation of a Poisson parameter is examined and compared with two linear unbiased estimations by mean squared error. Since a likelihood estimator is not explicitly available in general, a simulation study has been performed and the results are illustrated     

Improved estimators of a location vector with unknown scale parameter

The problem of estimating, under arbitrary quadratic loss, the location vector parameter θ of a p-variate distribution (p ≥ 3) with unknown covari-ance matrix ∑ = α² D (where D is a known diagonal matrix) is considered. A large class of improved shrinkage estimators is developed for this problem. This work generalizes results of Berger and Brandwein and Strawderman for the case of a known scale parameter and extends the authors’ results for the class of scale mixtures of normal distributions.     

The Extended Skew-Exponential Power Distribution and Its Derivation

We consider an extended family of asymmetric univariate distributions generated using a symmetric density, f, and the cumulative distribution function, G, of a symmetric distribution, which depends on two real-valued parameters λ and β and is such that when β = 0 it includes the entire class of distributions with densities of the form g(z | λ) = 2 G(λ z) f(z). A key element in the construction of random variables distributed according to the family is that they can be represented stochastically as the product of two random variables. From this representation we can readily derive theoretical properties, easy-to-implement simulation schemes, as well as extensions to the multivariate case and an explicit procedure for obtaining the moments. We give special attention to the extended skew-exponential power distribution. We derive its information matrix in order to obtain the asymptotic covariance matrix of the maximum likelihood estimators. Finally, an application to a real data set is reported, which shows that the extended skew-exponential power model can provide a better fit than the skew-exponential power distribution.     

A Distribution-Free Median-Unbiased Quantile Estimator

Given λ∈(0-,l), let x_λ(F) denote the unique λ-quantile of the distribution F. A distribution-free median-unbiased estimator of x_λ(F) is explicitly constructed     

OPTIMAL IV ESTIMATION OF SYSTEMS WITH STOCHASTIC REGRESSORS AND VAR DISTURBANCES WITH APPLICATIONS TO DYNAMIC SYSTEMS

This paper considers the general problem of Feasible Generalized Least Squares Instrumental Variables (FGLS IV) estimation using optimal instruments. First we summarize the sufficient conditions for the FGLS IV estimator to be asymptotically equivalent to an optimal GLS IV estimator. Then we specialize to stationary dynamic systems with stationary VAR errors, and use the sufficient conditions to derive new moment conditions for these models. These moment conditions produce useful IVs from the lagged endogenous variables, despite the correlation between errors and endogenous variables. This use of the information contained in the lagged endogenous variables expands the class of IV estimators under consideration and thereby potentially improves both asymptotic and small-sample efficiency of the optimal IV estimator in the class. Some Monte Carlo experiments compare the new methods with those of Hatanaka (1976). For the DGP used in the Monte Carlo experiments, asymptotic efficiency is strictly improved by the new IVs, and experimental small-sample efficiency is improved as well.     

Nonparametric estimators for shift in quantal bioassay

Let F(x) and F(x+θ) be log dose-response curves for a standard preparation and a test preparation, respectively, in a parallel quantal bioassay designed to test the relative potency of a drug, toxicant, or some other substance, and suppose the form of F is unknown. Several estimators of the shift parameter θ or relative potency, are compared, including some generalized and trimmed Spearman-Kärber estimators and a non parametric maximum likelihood estimator. Both point and interval estimation are discussed. Some recommendations concerning the choices of estimators are offered.     

On probit versus logit dynamic mixed models for binary panel data

In this paper, we consider inferences in a binary dynamic mixed model. The existing estimation approaches mainly estimate the regression effects and the dynamic dependence parameters either through the estimation of the random effects or by avoiding the random effects technically. Under the assumption that the random effects follow a Gaussian distribution, we propose a generalized quasilikelihood (GQL) approach for the estimation of the parameters of the dynamic mixed models. The proposed approach is computationally less cumbersome than the exact maximum likelihood (ML) approach. We also carry out the GQL estimation under two competitive, namely, probit and logit mixed models, and discuss both the asymptotic and small-sample behaviour of their estimators.     

On sequential estimation of the mean of a multidimensional gaussian process

For a multidimensional continuous time GAUssian process whose mean vector depends linearly of a multidimensional parameter, we consider a sequential estimation niodel. A suitable estimator process is constructed, and its distributions are analyzed in order to prove that it is progressively sufficient, After the reduction by sufficiency, an invariant structure is introduced and the optimal invariant terminal decision function is obtained.     

Use of satellite data in agricultural surveys

A major application of satellite remote sensing is the estimation of the acreage of agricultural crops. The potential for crop yield estimation using satellite remote sensing exists, but research in this area is still in its early stages. In this paper we survey the methodology for using remotely sensed data in agricultural surveys, based primarily on research conducted during the Large Area Crop Inventory Experiment (LACIE) and the follow-on program Agricultural Research and Inventory Surveys Through Aerospace Remote Sensing (AgRISTARS). The data obtained from multispectral scanner (MSS) and thematic mapper (TM) sensors onboard the Landsat series of satellites are described. Approaches for preprocessing, transferring, and modeling these data for understanding the relationship between their temporal behavior and crop growth cycles are discussed. Finally, techniques for crop identification and area and yield estimation are briefly described