The purpose of this paper is to draw attention to the widespread occurrence of quotient spaces in statistical work. Quotient spaces are intrinsic to probability distributions, residuals and interaction in linear models, covariance functions and variograms of stochastic processes, etc. The theme is that explicit recognition of the quotient space can offer surprising conceptual simplification. Th...

We statistically analysed the chemical components present in waste water from mines in Galicia (NW Spain). These elements pose a risk to public health and the environment, most particularly in the event of a failure in the containment structure of a pond or dam. The statistical processing of the data, which started with an analysis of the typical contaminants present in mining ponds and dams, p...

A constant linear model is a type of model that provides us with tools for drawing statistical inferences about means of random variables. Means of random variables are theoretical constructs, so we can never observe them directly but must instead use such tools to gain information about them. In this chapter, we define constant linear models and explore how to use them to address real-world qu...

Rural counties face unique challenges with regard to disaster vulnerability and resilience. We compared the quality of hazard mitigation plans (HMPs) completed in accordance with provisions of the Disaster Mitigation Act of 2000 from 21 urban and 36 rural counties in three southeastern states. HMPs were content analyzed to calculate a score for six principles of plan quality. Generalized linear...

In mixtures-of-experts (ME) models, "experts" of generalized linear models are combined, according to a set of local weights called the "gating function". The invariant transformations of the ME probability density functions include the permutations of the expert labels and the translations of the parameters in the gating functions. Under certain conditions, we show that the ME systems are iden...

in this work quantitative structure activity relationship (qsar) methodology was applied for modeling and prediction of cellular response to polymers that have been designed for tissue engineering. after calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regressions (mlr) and artificial neural network (ann) methods. the root m...

growth analysis is a valuable method in the quantitative analysis of crop growth, development and crop production. there are many regression models to describe the sigmoid growth patterns. by considering that, the parameters of non-linear regression models have physiological meanings, they are preferable relation to linear regression models. the aim of this study was to collect and evaluate the...

OBJECTIVE Variability in infant sleep and negative affective behavior (NAB) is a developmental phenomenon that has long been of interest to researchers and clinicians. However, analyses and delineation of such temporal patterns were often limited to basic statistical approaches, which may prevent adequate identification of meaningful variation within these patterns. Modern statistical procedure...

In this work some quantitative structure activity relationship models were developed for prediction of three bioenvironmental parameters of 28 volatile organic compounds, which are used in assessing the behavior of pollutants in soil. These parameters are; half-life, non dimensional effective degradation rate constant and effective Péclet number in two type of soil. The most effective descripto...

1 Introduction Why do we care about estimating production functions (in an IO course)? Production functions are components in many economic models; Evaluate e¢ ciency of an industry: – increasing returns, economies of scale – complementarity / substitutability between inputs – cost regulation, synergies and mergers – economics of scope – learning by doing Productivity analysis (output per index...