In contrast to the classical discrete choice experiment, the respondent in a rank-order discrete choice experiment, is asked to rank a number of alternatives instead of the preferred one. In this paper, we study the information matrix of a rank order nested multinomial logit model (RO.NMNL) and introduce local D-optimality criterion, then we obtain Locally D-optimal design for RO.NMNL models in...

Factor model is an appealing and effective analytic tool for high-dimensional time series, with a wide range of applications in economics, finance statistics. This paper develops two criteria the determination number factors tensor factor models where signal part observed series assumes Tucker decomposition core as tensor. The task to determine dimensions One proposed similar information based ...

A new model of an evolution ranks employees due to staff turnover in organization is designed. If the rank determined only by performance, shift incumbents proportional initial rank: status high reduced slightly. This effect that has been observed literature. However, if also depends on some other variable, positions may be greatly deteriorated. In case, nonlinearly time $\tau$ between subseque...

There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a flexible spatio-temporal prediction model that integrates land-use regression with...

Abstract. We propose a novel combination of the reduced basis method with low-rank tensor techniques for the efficient solution of parameter-dependent linear systems in the case of several parameters. This combination, called rbTensor, consists of three ingredients. First, the underlying parameter-dependent operator is approximated by an explicit affine representation in a low-rank tensor forma...

In this work, we propose an alternating low-rank decomposition (ALRD) approach and novel subspace algorithms for directionof-arrival (DOA) estimation. In the ALRD scheme, the decomposition matrix for rank reduction is composed of a set of basis vectors. A low-rank auxiliary parameter vector is then employed to compute the output power spectrum. Alternating optimization strategies based on recur...

The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance for US time series with the most promising existing alternatives, namely, factor models, large scale Bayesian VARs, and multivariate boosting. Speci…cally, we focus on classical reduc...