This chapter discusses the innovations of additional flexible methods for modeling an
individual term in an additive model. It focuses on how we fit additive models. A general and …

The assignment of alternatives (observations/objects) into predefined homogenous groups
is a problem of major practical and research interest. This type of problem is referred to as …

Partial Least Squares (PLS) is an estimation method and an algorithm for latent variable
path (LVP) models. PLS is a component technique and estimates the latent variables as …

Since most machine learning (ML) algorithms are designed for numerical inputs, efficiently
encoding categorical variables is a crucial aspect in data analysis. A common problem are …

Principal components analysis (PCA) is a well-known technique for approximating a tabular
data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets …

Locally weighted regression, or loess, is a way of estimating a regression surface through a
multivariate smoothing procedure, fitting a function of the independent variables locally and …

In marketing applications of structural equation models with unobservable variables,
researchers have relied almost exclusively on LISREL for parameter estimation. Apparently …

Comprehensive measurement of neural activity remains challenging due to the large
numbers of neurons in each brain area. We used volumetric two-photon imaging in mice …

In regression analysis the response variable Y and the predictor variables X 1…, Xp are
often replaced by functions θ (Y) and Ø1 (X 1),…, Ø p (Xp). We discuss a procedure for …

We estimate institutional investor preferences from proxy voting records. The W-NOMINATE
method maps investors onto a left-right dimension based on votes for fiscal year 2012 …