When using the K-nearest neighbours (KNN) method, one often ignores the uncertainty in the choice of K. To account for such uncertainty, Bayesian KNN (BKNN) has been proposed and studied (Holmes and Adams 2002 Cucala et al. 2009). We present some evidence to show that the pseudo-likelihood approach for BKNN, even after being corrected by Cucala et al. (2009), still significantly underest...

In modeling the rate of return associated with financial instruments, common probability distributions include the lognormal, gamma, and Weibull distributions. Furthermore, the method of maximum likelihood is widely used to estimate the unknown parameters of distributions due to the highly desirable properties of maximum likelihood estimators (MLEs). These properties include asymptotic unbiased...

The linear heteroskedastic regression model, for which the variance of the response is given by a suitable function of a set of linear exogenous variables, is very useful in econometric applications. We derive a simple matrix formula for the n biases of the maximum likelihood estimators of the parameters in the variance of the response, where n is the sample size. These biases are easily obtain...

Usually, the parameters of a Weibull distribution are estimated by maximum likelihood estimation. To reduce the biases of the maximum likelihood estimators (MLEs) of two-parameter Weibull distributions, we propose analytic bias-corrected MLEs. Two other common estimators of Weibull distributions, least-squares estimators and percentiles estimators, are also introduced. Based on a comparison of ...

In this paper, we propose a new class of asymptotically e¢ cient estimators for moment condition models. These estimators share the same higher order bias properties as the generalized empirical likelihood estimators and once bias corrected, have the same higher order e¢ ciency properties as the bias corrected generalized empirical likelihood estimators. Unlike the generalized empirical likelih...