Neural Networks and Applications

Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach

Introduction * Information and Likelihood Theory: A Basis for Model Selection and Inference * Basic Use of the Information-Theoretic Approach * Formal Inference From More Than One Model: Multi-Model Inference (MMI) * Monte Carlo Insights and Extended Examples * Statistical Theory and Numerical Results * Summary

Investigating Causal Relations by Econometric Models and Cross-spectral Methods

There occurs on some occasions a difficulty in deciding the direction of causality between two related variables and also whether or not feedback is occurring. Testable definitions of causality and feedback are proposed and illustrated by use of simple two-variable models. The important problem of apparent instantaneous causality is discussed and it is suggested that the problem often arises due to slowness in recording information or because a sufficiently wide class of possible causal variables has not been used. It can be shown that the cross spectrum between two variables can be decomposed into two parts, each relating to a single causal arm of a feedback situation. Measures of causal lag and causal strength can then be constructed. A generalisation of this result with the partial cross spectrum is suggested.

Regression and time series model selection in small samples

A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The correction is of particular use when the sample size is small, or when the number of fitted parameters is a moderate to large fraction of the sample size. The corrected method, called AICC, is asymptotically efficient if the true model is infinite dimensional. Furthermore, when the true model is of finite dimension, AICC is found to provide better model order choices than any other asymptotically efficient method. Applications to nonstationary autoregressive and mixed autoregressive moving average time series models are also discussed.

Modeling by shortest data description

Mathematical Methods of Statistics.

Journal Article Mathematical Methods of Statistics Get access Mathematical Methods of Statistics. By HAROLD CRAMER. Princeton University Press (London : Geoffrey Cumberlege), 1946. Pp. xvi + 575. 33s. 6d.) R. C. Geary R. C. Geary Department of Applied Economics, Cambridge Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 57, Issue 226, 1 June 1947, Pages 200–202, https://doi.org/10.2307/2226151 Published: 01 June 1947

Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering

Model Selection and Inference