Nonparametric Conditional Inference for Regression Coefficients with Application to Configural Polysampling

We consider inference procedures, conditional on an observed ancillary statistic, for regression coefficients under a linear regression setup where the unknown error distribution is specified nonparametrically. We establish conditional asymptotic normality of the regression coefficient estimators under regularity conditions, and formally justify the approach of plugging in kernel-type density estimators in conditional inference procedures. Simulation results show that the approach yields accurate conditional coverage probabilities when used for constructing confidence intervals. The plug-in approach can be applied in conjunction with configural polysampling to derive robust conditional estimators adaptive to a confrontation of contrasting scenarios. We demonstrate this by investigating the conditional mean squared error of location estimators under various confrontations in a simulation study, which successfully extends configural polysampling to a nonparametric context

A robust conditional approximation of marginal tail probabilities.

The aim of this contribution is to derive a robust approximate conditional procedure used to eliminate nuisance parameters in regression and scale models. Unlike the approximations to exact conditional solutions based on the likelihood function and on the maximum likelihood estimator, the robust conditional approximation of marginal tail probabilities does not suffer from lack of robustness to model misspecification. To assess the performance of the proposed robust conditional procedure the results of sensitivity analyses are discussed

Recent advances in imprecise-probabilistic graphical models

We summarise and provide pointers to recent advances in inference and identification for specific types of probabilistic graphical models using imprecise probabilities. Robust inferences can be made in so-called credal networks when the local models attached to their nodes are imprecisely specified as conditional lower previsions, by using exact algorithms whose complexity is comparable to that for the precise-probabilistic counterparts

Post Loss/:Profit Announcement Drift

We document a market failure to fully respond to loss/profit quarterly announcements. The annualized post portfolio formation return spread between two portfolios formed on extreme losses and extreme profits is approximately 21 percent. This loss/profit anomaly is incremental to previously documented accounting-related anomalies, and is robust to alternative risk adjustments, distress risk, firm size, short sales constraints, transaction costs, and sample periods. In an effort to explain this finding, we show that this mispricing is related to differences between conditional and unconditional probabilities of losses/profits, as if stock prices do not fully reflect conditional probabilities in a timely fashion

Modeling Probabilistic Networks of Discrete and Continuous Variables

AbstractIn this paper we show how discrete and continuous variables can be combined using parametric conditional families of distributions and how the likelihood weighting method can be used for propagating uncertainty through the network in an efficient manner. To illustrate the method we use, as an example, the damage assessment of reinforced concrete structures of buildings and we formalize the steps to be followed when modeling probabilistic networks. We start with one set of conditional probabilities. Then, we examine this set for uniqueness, consistency, and parsimony. We also show that cycles can be removed because they lead to redundant probability information. This redundancy may cause inconsistency, hence the probabilities must be checked for consistency. This examination may require a reduction to an equivalent set instandard canonicalform from which one can always construct a Bayesian network, which is the most convenient model. We also perform a sensitivity analysis, which shows that the model is robust