This paper presents an original approach for jointly fitting survival times and classifying samples into subgroups. The Coxlogit model is a generalized linear model with a common set of selected features for both tasks. Survival times and class labels are here assumed to be conditioned by a common risk score which depends on those features. Learning is then naturally expressed as maximizing the...

We exhibit efficient algorithms to perform the following task: Given a function f defined on a finite subset E ⊂ R, compute a C function F on R, with a controlled C norm, that approximates f on the subset E.

In this course we will be studying the subgroup structure of the finite alternating and symmetric groups. What does the phrase “study the subgroups of symmetric groups” mean? In this introduction I’ll suggest an answer to that question, and attempt to convince you that answer has some merit. In the process you’ll get some idea of the material we will be covering, and I’ll attempt to motivate th...

This paper analyzes linear least squares problems with absolute quadratic constraints. We develop a generalized theory following Bookstein’s conic-fitting and Fitzgibbon’s direct ellipse-specific fitting. Under simple preconditions, it can be shown that a minimum always exists and can be determined by a generalized eigenvalue problem. This problem is numerically reduced to an eigenvalue problem...

The use of generalized additive models in statistical data analysis suffers from the restriction to few explanatory variables and the problems of selection of smoothing parameters. Generalized additive model boosting circumvents these problems by means of stagewise fitting of weak learners. A fitting procedure is derived which works for all simple exponential family distributions, including bin...

This short course covers the application of the statistical theory of extreme values to climate, in general, and to climate change, in particular. The statistical theory of extreme values is briefly reviewed, both the extremal types theorem with its application via the block maxima approach (i.e., fitting the generalized extreme value distribution) and the point process model with its applicati...

Generalized and subset generalized bilinear models which are capable of achieving stationary for all non-linear series were developed to accommodate error process of elements of 2q-1 possible subsets with a view to achieve better model in the generalized bilinear model. The parameters of the models are estimated using robust nonlinear least-square method and Newton-Raphson iterative method. An ...

The use of generalized additive models in statistical data analysis suffers from the restriction to few explanatory variables and the problems of selection of smoothing parameters. Generalized additive model boosting circumvents these problems by means of stagewise fitting of weak learners. A fitting procedure is derived which works for all simple exponential family distributions, including bin...

Regardless of whether the eation of concrete social sensitivity to poor quality the details of specific netw tions where choices cann of tackling these problem of an underlying affect di moment correlations and the sum of observed relat used to show the value of relations are replaced by u also within a broader pro made for future work. Following the introduction of blockmodeling by Lorrain and...