The main object of investigation in this paper is a very general regression model optional setting - when an observed process semimartingale depending on unknown parameter. It well-known that statistical data may present information flow/filtration without usual conditions. estimation problem achieved by means structural least squares (LS) estimates and their sequential versions. results the ar...

https://rde.ac Logistic regression is a regression model where the dependent variable is categorical and corresponding independent variables can be categorical or continuous. This article covers the case of a binary dependent variable such as an event occurring coded 1 = ‘event’ and 0 = ‘no event’. Frequent outcomes are pass/fail, win/lose, disease/no disease, etc. The logistic regression model...

Statistical graphics play a crucial role in exploratory data analysis, model checking and diagnosis. Until recently there were no formal visual methods in place for determining statistical significance of findings. This changed, when Buja et al. [2009] conceptually introduced two protocols for formal tests of visual findings. In this paper we take this a step further by comparing the lineup pro...

Semi-supervised methods use unlabeled data in addition to labeled data to construct predictors. While existing semi-supervised methods have shown some promising empirical performance, their development has been based largely based on heuristics. In this paper we study semi-supervised learning from the viewpoint of minimax theory. Our first result shows that some common methods based on regulari...

Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.

Our work aims at facilitating the schedulability analysis of non-critical systems, in particular those that have soft real-time constraints, where WCETs can be replaced by less stringent probabilistic bounds, which we call Maximal Execution Times (METs). In our approach, we can obtain adequate probabilistic execution time models by separating the non-random input data dependency from a modeling...