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Universitat de Girona. Departament dâ€™InformÃ tica i MatemÃ tica Aplicada  
Bergman, Jakob
Holmquist, B. 

2018 June 5  
Cleveland (1979) is usually credited with the introduction of the locally weighted regression, Loess. The concept was further developed by Cleveland and Devlin (1988). The general idea is that for an arbitrary number of explanatory data points xi the value of a dependent variable is estimated Ë†yi . The Ë†yi is the fitted value from a dth degree polynomial in xi . (In practice often d = 1.) The Ë†yi is fitted using weighted least squares, WLS, where the points xk (k = 1, . . . , n) closest to xi are given the largest weights. We define a weighted least squares estimation for compositional data, CWLS. In WLS the sum of the weighted squared Euclidean distances between the observed and the estimated values is minimized. In CWLS we minimize the weighted sum of the squared simplicial distances (Aitchison, 1986, p. 193) between the observed compositions and their estimates. We then define a compositional locally weighted regression, CLoess. Here a composition is assumed to be explained by a real valued (multivariate) variable. For an arbitrary number of data points xi we for each xi fit a dth degree polynomial in xi yielding an estimate Ë†yi of the composition yi . We use CWLS to fit the polynomial giving the largest weights to the points xk (k = 1, . . . , n) closest to xi . Finally the CLoess is applied to Swedish opinion poll data to create a pollofpolls time series. The results are compared to previous results not acknowledging the compositional structure of the data  
http://hdl.handle.net/2072/319410  
eng  
Universitat de Girona. Departament dâ€™InformÃ tica i MatemÃ tica Aplicada  
Tots els drets reservats  
EstadÃstica matemÃ tica  Congressos
Mathematical statistics  Congresses AnÃ lisi multivariable  Congressos Multivariate analysis  Congresses 

Compositional Loess Modelling  
info:eurepo/semantics/conferenceObject  
Recercat 