The EM algorithm is a popular and useful algorithm for "nding the maximum likelihood estimator in incomplete data problems. Each iteration of the algorithm consists of two simple steps: an E-step, in which a conditional expectation is calculated, and an M-step, where the expectation is maximized. In some problems, however, the EM algorithm cannot be applied since the conditional expectation req...

This paper reviews the literature on tests for the correct specification of the functional form of parametric conditional expectation and conditional distribution models. In particular I will discuss various versions of the Integrated Conditional Moment (ICM) test and the ideas behind them.

In this paper we propose a consistent Integrated Conditional Moment (ICM) test of the functional form of a conditional heteroskedasticity model, for example a GARCH specification, which is asymptotically independent of the ICM test of the specification of the underlying conditional expectation model, under the null hypothesis that both models are correctly specified.

The paper considers estimating a parameter β that defines an estimating function U.y,x,β/ for an outcome variable y and its covariate x when the outcome is missing in some of the observations.We assume that, in addition to the outcome and the covariate, a surrogate outcome is available in every observation. The efficiency of existing estimators for β depends critically on correctly specifying t...

We use the conditional distribution and conditional expectation of one random variable given the other one being large to capture the strength of dependence in the tails of a bivariate random vector. We study the tail behavior of the boundary conditional cumulative distribution function (cdf) and two forms of conditional tail expectation (CTE) for various bivariate copula families. In general, ...