Adaptive Linear Regression

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10507332" target="_blank" >RIV/00216208:11320/25:10507332 - isvavai.cz</a>
Result on the web
<a href="http://10.1007/978-3-662-69359-9_9" target="_blank" >http://10.1007/978-3-662-69359-9_9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-662-69359-9_9" target="_blank" >10.1007/978-3-662-69359-9_9</a>

Result language
angličtina
Original language name
Adaptive Linear Regression
Original language description
Consider a set of data consisting of n observations of a response variable Y and of vector of p explanatory variables X = ( X 1, X 2, ..., X p ) DOWN TACK. Their relationship is described by the linear regression modelIn terms of the observed data, the model isThe variables e 1, ..., e n are unobservable model errors, which are assumed being independent and identically distributed random variables with a distribution function F and density f. The density is unknown, we only assume that it is symmetric around 0. The vector β = ( β 1, β 2, ..., β p ) DOWN TACK is an unknown parameter, and the problem of interest is to estimate β based on observations Y 1, ..., Y n and x i = ( x i1 , ..., x ip ) DOWN TACK, i = 1, ..., n.Besides the classical least squares...
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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