Publication

Additive Smooth Modelling with Splines

2014
Reports

Abstract

Linear regression summarises the link between a variable of interest and one or several explanatory variables through a parametric relationship. Hastie and Tibshirani (1986) extended this approach by allowing a non-parametric description of the dependence between the variable of interest and the explanatory variables. In this report, we review the notion of smooths, present different approaches to selection of the smoothing parameter, and describe several spline bases, in the lines of Wood (2006). We show evidence of smoking as a cause of deaths from larynx cancer by applying Gaussian Markov random fields and thin plate regression splines on data covering 544 German districts from 1986 to 1990.

Official source

https://infoscience.epfl.ch/entities/publication/760c272a-77f3-4dbb-b857-600abfd9f69b

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2014
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/760c272a-77f3-4dbb-b857-600abfd9f69b

About this result

Related concepts (32)

Nonparametric statistics

Nonparametric statistics is the type of statistics that is not restricted by assumptions concerning the nature of the population from which a sample is drawn. This is opposed to parametric statistics, for which a problem is restricted a priori by assumptions concerning the specific distribution of the population (such as the normal distribution) and parameters (such the mean or variance).

Dependent and independent variables

Dependent and independent variables are variables in mathematical modeling, statistical modeling and experimental sciences. Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of the experiment in question. In this sense, some common independent variables are time, space, density, mass, fluid flow rate, and previous values of some observed value of interest (e.

Categorical variable

In statistics, a categorical variable (also called qualitative variable) is a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or on the basis of some qualitative property. In computer science and some branches of mathematics, categorical variables are referred to as enumerations or enumerated types. Commonly (though not in this article), each of the possible values of a categorical variable is referred to as a level.

Related publications (1)

Additive Smooth Modelling with Splines

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