Statistical methodology is presented for the regression analysis of multiple events in the presence of random eeects and measurement error. Omitted covariates are modeled as random eeects. Our approach to parameter estimation and signiicance testing is to start with a naive model of semi-parametric Poisson process regression, and then to adjust for random eeects and any possible covariate measu...

1.3. Statistical analysis & Expert systems in toxicological screening: 1-9 1.3.1. CLOUDS-Overlap 1-10 1.3.2. SMART scaling 1-12 1.3.3. Commonly used terms in statistical pattern recognition 1-12 1.3.3.1. Cross-validation 1-12 1.3.3.2. Classification vs. regression 1-12 1.3.3.3. Supervised vs. unsupervised methods 1-13 1.3.4. Pattern recognition techniques 1-13 1.3.5. Principle component analysi...

This study examined various regression-based techniques and an artificial neural network used for streamflow forecasting during typhoons. A flow hydrograph was decomposed into two segments, rising and falling limbs, and the individual segments were modeled using statistical techniques. In addition, a conceptual rainfallerunoff model, namely the Public Works Research Institute (PWRI)-distributed...

In this paper, we present techniques for steganalysis of images that have been potentially subjected to a watermarking algorithm. We show that watermarking schemes leave statistical evidence or structure that can be exploited for detection with the aid of proper selection of image features and multivariate regression analysis. We use some image quality metrics as the feature set to distinguish ...

In this article, the performance of data mining and statistical techniques was empirically compared while varying the number of independent variables, the types of independent variables, the number of classes of the independent variables, and the sample size. Our study employed 60 simulated examples, with artificial neural networks and decision trees as the data mining techniques, and linear re...

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression ...

Methods of estimation of density and regression function are quite common in statistical applications. Wavelet theory has the potential to provide statisticians with powerful new techniques for nonparametric inference. It combines recent advances in approximation theory with insights gained from applied signal analysis. Nonparametric curve estimation by wavelets has been treated in numerous art...

Statistics An Intduction to Stistical Lerning with Applications in R An Introduction to Statistical Learning provides an accessible overview of the fi eld of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fi elds ranging from biology to fi nance to marketing to astrophysics in the past twenty years. Th is book presents some of ...