Classical least squares regression consists of minimizing the sum of the squared residuals. Many authors have produced more robust versions of this estimator by replacing the square by something else, such as the absolute value. In this article a different approach is introduced in which the sum is replaced by the median of the squared residuals. The resulting estimator can resist the effect of...

To explore multi-way data, different methods have been proposed. Here, we study the popular PARAFAC (Parallel factor analysis) model, which expresses multi-way data in a more compact way, without ignoring the underlying complex structure. To estimate the score and loading matrices, an alternating least squares procedure is typically used. It is however well known that least squares techniques s...

Products' packaging attributes can predispose the consumer to purchase whilst products' sensory attributes con®rm liking and may determine repeat purchases. Products should, therefore, convey their sensory character by their pack and, by doing so, consumer expectations generated by the pack will be met. In this study, a trained panel measured the packaging attributes of Cheddar cheese by descri...

In this article, we study large-dimensional matrix factor models and estimate the loading matrices score by minimizing square loss function. Interestingly, resultant estimators coincide with Projected Estimators (PE) in Yu et al. which was proposed from perspective of simultaneous reduction dimensionality magnitudes idiosyncratic error matrix. other word, provide a least-square interpretation P...

This paper is a part of study about the impact of the microclimate on the space use and the user’s behaviours. The purpose of our study is to get an in-depth understanding of how people use out door public spaces in different climatic conditions, how their behaviour and perception of the space change with the climatic variations. We have employed a multi-method approach which combines a “qualit...