We investigate the family of conditional independence models require constraints on complete but non hierarchical marginal log-linear parameters; by exploiting results on the mixed parameterization, we show that they are smooth when the jacobian of a reconstruction algorithm has spectral radius strictly less than 1. This condition is always satisfies in simple contexts where only two marginals ...

Probabilistic Inference Networks are becoming increasingly popular for modeling and reasoning in uncertain domains. In the past few years, many efforts have been made in learning the parameters of probabilistic network directly from databases. The learning techniques are based on the assumption that the database is complete. If there are missing values, these values are first estimated and then...

In this paper, we adapt a mixture model originally developed for regression models with independent data for the more general case of correlated outcome data, which includes longitudinal data as a special case. The estimation is performed by a gener-alisation of the EM algorithm which we call the Expectation-Solution (ES) algorithm. In this ES algorithm the M-step of the EM algorithm is replace...

In prediction problems with more predictors than observations, it can sometimes be helpful to use a joint probability model, π(Y,X), rather than a purely conditional model, π(Y | X), where Y is a scalar response variable and X is a vector of predictors. This approach is motivated by the fact that in many situations the marginal predictor distribution π(X) can provide useful information about th...

This paper estimates the dynamic effects of marginal tax rate changes on income reported on tax returns in the United States over the 1950-2010 period. After isolating exogenous variation in average marginal tax rates in structural vector autoregressions using a narrative identification approach, I find large positive effects in the top 1% of the income distribution. In contrast to earlier find...

In this paper, we consider identification and estimation of average marginal effects in a correlated random effects model without imposing strong functional form assumptions on the structural likelihood or the mixing distribution. Identification is achieved through imposing that the mixing distribution depends on observed covariates only through an index function and the assumption that at leas...

Mixed effects models are frequently used for analyzing longitudinal data. Normality assumption of random effects distrbution is a routine assumption for these models, violation of which leads to model misspecification and misleading parameter estimates. We propose a semi-parametric approach using gradient function for random effect estimation. In the approach, we relax the normality assumption ...

In right-censored data, one goal is to to obtain an estimator of treatment dose-effect, which is represented by some parameter in a marginal model of lifetime given treatment dose variable. When dependent censoring is explained by both the dose variable and many other auxiliary covariates (high-dimensional auxiliary information), an intuitive approach to estimate the dose effect is to first est...

The extremal index θ, a measure of the degree of local dependence in the extremes of a stationary process, plays an important role in extreme value analyses. We estimate θ semiparametrically, using the relationship between the distribution of block maxima and the marginal distribution of a process to define a semiparametric model. We show that these semiparametric estimators are simpler and sub...

Background and Aim: Marginal adaptation has a significant role in the success and longevity of indirect restorations. This experimental study compared the effect of intraoral and extraoral scanning on the marginal adaptation of the crowns which was made using the CEREC AC system. Materials and Methods: A Typodont maxillary first molar was prepared and served as the master die for an all-cera...