1. Raymond Myers

Bookmark Author Subjects Contents. Machine derived contents note: 1. Introduction: Regression Analysis 2. The Simple Linear Regression Model 3. The Multiple Linear Regression Model 4.

Criteria for Choice of Best Model 5. Analysis of Residuals 6. Influence Diagnostics 7. Nonstandard Conditions, Violations of Assumptions, and Transformations 8. Detecting and Combating Multicollinearity 9. Nonlinear Regression Appendix A: Some Special Concepts in Matrix Algebra / Appendix B: Some Special Manipulations / Appendix C: Statistical Tables.Bookmark Work ID 5631963.

This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control. Configuration of the computing and communications systems found at home and in the workplace is a complex task that currently requires the attention of the user. Recently, researchers have begun to examine computers that would autonomously change their functionality based on observations of who or what was around them. By determining their context, using input from sensor systems distributed throughout the environment, computing devices could personalize themselves to their current user, adapt their behavior according to their location, or react to their surroundings.

The authors present a novel sensor system, suitable for large-scale deployment in indoor environments, which allows the locations of people and equipment to be accurately determined. We also describe some of the context-aware applications that might make use of this fine-grained location information. Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions.

At its core, it employs nonparametric regression with locally linear models. In order to stay computationally e#cient and numerically robust, each local model performs the regression analysis with a small number of univariate regressions in selected directions in input space in the spirit of partial least squares regression. We discuss when and how local learning techniques can successfully work in high dimensional spaces and review the various techniques for local dimensionality reduction before finally deriving the LWPR algorithm. There has been much publicity about the ability of artificial neural networks to learn and generalize. In fact, the most commonly used artificial neural networks, called multilayer perceptrons, are nothing more than nonlinear regression and discriminant models that can be implemented with standard statistical software.

This paper explains what neural networks are, translates neural network jargon into statistical jargon, and shows the relationships between neural networks and statistical models such as generalized linear models, maximum redundancy analysis, projection pursuit, and cluster analysis. Ramones rocket to russia remastered rar extractor. Hong Kong Universig qfScience and Technolng) This paper examines the contribution of each of the four dimen-sions in Thomas and Velthouse’s (1990) multidimensional conceptual-ization of psychological empowerment in predicting three expected outcomes of empowerment: effectiveness, work satisfaction, and job-related strain.

The literature on the four dimensions of empowerment (i.e., meaning, competence, self-determination, and impact) is reviewed and theoretical logic is developed linking the dimensions to specific outcomes. The expected relationships are tested on a sample of manag-ers from diverse units of a manufacturing organization and then repli-cated on an independent sample of lower-level employees in a service organization using alternative measures of the outcome variables. The results, largely consistent across the two samples, suggest that dcrerent dimensions are related to different outcomes and that no single dimen-sion predicts all three outcomes. These results indicate that employees need to experience each of the empowerment dimensions in order to achieve all of the hoped for outcomes of empowerment. We describe a feature selection method that can be applied directly to models that are linear with respect to their parameters, and indirectly to others.

It is independent of the target machine. It is closely related to classical statistical hypothesis tests, but it is more intuitive, hence more suitable for use by engineers who are not statistics experts. Furthermore, some assumptions of classical tests are relaxed. The method has been used successfully in a number of applications that are briefly described. Abstract: In recent years, an increasing number of research projects investigated whether the central nervous system employs internal models in motor control. While inverse models in the control loop can be identified more readily in both motor behavior and the firing of single neurons, providing direct evidence for the existence of forward models is more complicated.

Raymond Myers

In this paper, we will discuss such an identification of forward models in the context of the visuomotor control of an unstable dynamic system, the balancing of a pole on a finger. Pole balancing imposes stringent constraints on the biological controller, as it needs to cope with the large delays of visual information processing while keeping the pole at an unstable equilibrium. We hypothesize various model-based and nonmodel-based control schemes of how visuomotor control can be accomplished in this task, including Smith Predictors, predictors with Kalman filters, tapped-delay line control, and delay-uncompensated control.

Behavioral experiments with human participants allow exclusion of most of the hypothesized control schemes. In the end, our data supports the existence of a forward model in the sensory preprocessing loop of control. As an important part of our research, we will provide a discussion of when and how forward models can be identified, and also the possible pitfalls in the search for forward models in control.

Abstract—Quantitative structure-activity relationships are mathematical models constructed based on the hy-pothesis that structure of chemical compounds is related to their biological activity. A linear regression model is often used to estimate and/or to predict the nature of the relationship between a measured activity and some measure or calculated descriptors. Linear regression helps to answer main three questions: does the biological activity depend on structure information; if so, the nature of the relationship is linear; and if yes, how good is the model in prediction of the biological activity of new compound(s). This manuscript presents the steps on linear regression analysis moving from theoretical knowledge to an example conducted on sets of endocrine disrupting chemicals. Keywords-robust regression; validation; diagnostic; pre. The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete-1 cross validation concept and the associated leave-one-out test error also known as the predicted residual sums of squares (PRESS) statistic, without resorting to any other validation data set for model evaluation in the model construction process.

Computational efficiency is ensured using an orthogonal forward regression, but the algorithm incrementally minimizes the PRESS statistic instead of the usual sum of the squared training errors. A local regularization method can naturally be incorporated into the model selection procedure to further enforce model sparsity. The proposed algorithm is fully automatic, and the user is not required to specify any criterion to terminate the model construction procedure. Comparisons with some of the existing state-of-art modeling methods are given, and several examples are included to demonstrate the ability of the proposed algorithm to effectively construct sparse models that generalize well.