Nonlinear mixed‐effects models with misspecified random‐effects distribution

Concentration of Posterior Distributions with Misspecified Models

Abstract We investigate the asymptotic properties of posterior distributions when the model is misspecified, i.e. it is comtemplated that the observations x1, ..., xn might be drawn from a density in a family {hσ,σ ∈ Θ} where Θ ⊂ IR, while the actual distribution of the observations may not correspond to any of the densities hσ . A concentration property around a fixed value of the parameter is...

Forecasting and Signal Extraction with Misspecified Models

The paper illustrates and compares estimation methods alternative to maximum likelihood, among which multistep estimation and leave-one-out cross-validation, for the purposes of signal extraction, and in particular the separation of the trend from the cycle in economic time series, and longrange forecasting, in the presence of a misspecified, but simply parameterised model. Our workhorse models...

Active Learning for Misspecified Models

Active learning is the problem in supervised learning to design the locations of training input points so that the generalization error is minimized. Existing active learning methods often assume that the model used for learning is correctly specified, i.e., the learning target function can be expressed by the model at hand. In many practical situations, however, this assumption may not be fulf...

Hydrogen Distribution in Refinery with Nonlinear Programming

Comparisons among Some Estimators in Misspecified Linear Models with Multicollinearity

In this paper we deal with comparisons among several estimators available in situations of multicollinearity (e.g., the r k class estimator proposed by Baye and Parker, the ordinary ridge regression (ORR) estimator, the principal components regression (PCR) estimator and also the ordinary least squares (OLS) estimator) for a misspecified linear model where misspecification is due to omission of...