The Wiley-Interscience Paperback Series consists of selected books that have been made
more accessible to consumers in an effort to increase global appeal and general circulation …

Treats linear regression diagnostics as a tool for application of linear regression models to
real-life data. Presentation makes extensive use of examples to illustrate theory. Assesses …

The boxplot method (Exploratory Data Analysis, Addison-Wesley, Reading, MA, 1977) is a
graphically-based method of identifying outliers which is appealing not only in its simplicity …

Growth-curve models are generalized multivariate analysis-of-variance models. The basic
idea of the models is to use different polynomials to fit different treatment groups involved in …

In this article, we consider simultaneous outlier identification rules for multivariate data,
generalizing the concept of so-called α outlier identifiers, as presented by Davies and …

This work examines theoretical issues, as well as practical developments in statistical
inference related to econometric models and analysis. This work offers discussions on such …

The identification of contaminated observations or outliers is an important part of data
analysis since such observations can have a profound influence and distort the analysis …

This work focuses on short-term photovoltaic (PV) power forecasting each 5 min of the
following day of a 5.94 kWp grid-connected PV plant located in Safi, Morocco. The PV …

Publisher Summary This chapter discusses the general principle of outlier generating
models. It describes four main types of outlier identification rules—namely, block …

It is important to identify outliers since inclusion, especially when using parametric methods,
can cause distortion in the analysis and lead to erroneous conclusions. One of the easiest …