We propose a likelihood-based approach to Stochastic Non-Parametric Envelopment of Data (StoNED) estimator using directional distance function with firm-specific vectors. Additionally, we show how estimate inefficiency estimates instead focusing on their average only. Moreover, models that are robust misspecification in general and the use unit-information-priors this class models. These priors...

Semi-supervised learning plays an important role in large-scale machine learning. Properly using additional unlabeled data (largely available nowadays) often can improve the machine learning accuracy. However, if the machine learningmodel is misspecified for the underlying true data distribution, the model performance could be seriously jeopardized. This issue is known as model misspecification...

We present a multichannel, model-free method for estimation of event-related potential (ERP) amplitude ratios and amplitudes using singular value decomposition (SVD), and compare with the Dipole Components Model (DCM). When the ERPs are generated by a single or multiple dipoles with equal amplitude ratios, the SVD method is superior to DCM in terms of reliable estimation of amplitude and is com...

We introduce estimation and test procedures through divergence minimization for models satisfying linear constraints with unknown parameter. These procedures extend the empirical likelihood (EL) method and share common features with generalized empirical likelihood (GEL) approach. We treat the problems of existence and characterization of the divergence projections of probability measures on se...

The independent additive errors linear model consists of a structure for the mean and a separate structure for the error distribution. The error structure may be parametric or it may be semiparametric. Under alternative values of the mean structure, the best fitting additive errors model has an error distribution which can be represented as the convolution of the actual error distribution and t...

We discuss robust designs for generalized linear models with protection for possible departures from the usual model assumptions. Besides possible inaccuracy in an assumed linear predictor, both problems of overdispersion and misspecification in link function are addressed. For logistic and Poisson models, as examples, we incorporate the variance function prescribed by a superior model similar ...

This study presents some quantitative evidence from a number of simulation experiments on the accuracy of the productivity growth estimates derived from growth accounting (GA) and frontier-based methods (namely Data envelopment Analysis-, Corrected ordinary least squares-, and Stochastic Frontier Analysis-based Malmquist indices) under various conditions. These include the presence of technical...

Simon’s and Theil’s certainty equivalence property justifies a convenient algorithm for solving dynamic programming problems with quadratic objectives and linear transition laws: first, optimize under perfect foresight, then substitute optimal forecasts for unknown future values. A similar decomposition into separate optimization and forecasting steps prevails when a decision maker wants a deci...