toxicity of 38 aliphatic carboxylic acids was studied using non-linear quantitative structure-toxicityrelationship (qstr) models. the adaptive neuro-fuzzy inference system (anfis) was used to construct thenonlinear qstr models in all stages of study. two anfis models were developed based upon differentsubsets of descriptors. the first one used log ow k and lumo e as inputs and had good predicti...

The core problem of statistics and machine learning addresses the question how can we efficiently find a statistical model to describe empirical data. Classical statistical approaches to solve this problem have been complemented during the last 15 years by Neural Computation, a very promising strategy to data analysis. The Dagstuhl seminar on “Inference Principles and Model Selection” — the fou...

This paper studies inference about the values of the parameters in the threshold model in a generalized method of moments (GMM) framework. First, we establish that the extensively studied least squares method leads to substantially oversized tests and confidence intervals when the coeffi cient change is not large. Second, by re-ordering the data to recast the threshold model as a structural bre...

Two nested or non-nested candidate sampling models for an observed data set may be compared by consideration of summaries of a probability plot, which contrasts the posterior quantiles of the log-likelihoods under the two models. The procedures address both preference inference and refutation inference, and extensions to DIC and alternatives to AIC are developed. Preference inference favors mod...

Graphical modeling is a method that is increasingly being used to model real-life applications such as medical diagnosis, security and emergency response systems, and computer vision. A graphical model represents random variables and dependencies between them by means of a graph, in which some variables may not be observable in practice. An important problem in graphical models is that of infer...

Abstract. A case of the matrix Kummer relation of Herz (1955) based on the Pearson VII type matrix model is derived in this paper. As a con- sequence, the polynomial Pearson VII configuration density is obtained and this sets the corresponding exact inference as a solvable aspect in shape theory. An application in postcode recognition, including a nu- merical comparison between the exact poly...

To perform inference after model selection, we propose controlling the selective type I error; i.e., the error rate of a test given that it was performed. By doing so, we recover long-run frequency properties among selected hypotheses analogous to those that apply in the classical (non-adaptive) context. Our proposal is closely related to data splitting and has a similar intuitive justification...

Conventional statistical inference requires that a model of how the data were generated be known before the data are analyzed. Yet in criminology, and in the social sciences more broadly, a variety of model selection procedures are routinely undertaken followed by statistical tests and confidence intervals computed for a “final” model. In this paper, we examine such practices and show how they ...

Contrasting “geometric fitting”, for which the noise level is taken as the asymptotic variable, with “statistical inference”, for which the number of observations is taken as the asymptotic variable, we give a new definition of the “geometric AIC” and the “geometric MDL” as the counterparts of Akaike’s AIC and Rissanen’s MDL. We discuss various theoretical and practical problems that emerge fro...