Likelihood-based regression models such as the normal linear regression model and the linear logistic model, assume a linear (or some other parametric) form for the covariates $X_1, X_2, \cdots, X_p$. We introduce the class of generalized additive models which replaces the linear form $\sum \beta_jX_j$ by a sum of smooth functions $\sum s_j(X_j)$. The $s_j(\cdot)$'s are unspecified functions that are estimated using a scatterplot smoother, in an iterative procedure we call the local scoring algorithm. The technique is applicable to any likelihood-based regression model: the class of generalized linear models contains many of these. In this class the linear predictor $\eta = \Sigma \beta_jX_j$ is replaced by the additive predictor $\Sigma s_j(X_j)$; hence, the name generalized additive models. We illustrate the technique with binary response and survival data. In both cases, the method proves to be useful in uncovering nonlinear covariate effects. It has the advantage of being completely automatic, i.e., no "detective work" is needed on the part of the statistician. As a theoretical underpinning, the technique is viewed as an empirical method of maximizing the expected log likelihood, or equivalently, of minimizing the Kullback-Leibler distance to the true model.

This survey recounts contributions to the axiomatic foundations of subjective probability from the pioneering era of Ramsey, de Finetti, Savage, and Koopman to the mid-1980's. It is designed to be accessible to readers who have little prior acquaintance with axiomatics. At the same time, it provides a fairly complete picture of the present state of the measurement-theoretic foundations of subjective probability.

Laplace's first major article on mathematical statistics was published in 1774. It is arguably the most influential article in this field to appear before 1800, being the first widely read presentation of inverse probability and its application to both binomial and location parameter estimation. After a brief introduction, and English translation of this epochal memoir is given.

A bewilderingly large number of statistical quantities have been proposed to study outliers and influence of individual observations in regression analysis. In this article we describe the inter-relationships which exist among the proposed measures. An examination of these relationships leads us to conclude that only three of these measures along with some graphical displays can provide an analyst a complete picture of outliers (major discrepant points) and points which excessively influence the fitted regression equation. Illustrative examples based on real data are presented.