Sökning: "principal components regression"

Visar resultat 1 - 5 av 24 avhandlingar innehållade orden principal components regression.

  1. 1. Regression methods in multidimensional prediction and estimation

    Författare :Anders Björkström; Rolf Sundberg; Philip J Brown; Stockholms universitet; []
    Nyckelord :NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; regression; prediction; principal compnents regression; ridge regression; partial least squares; Mathematical statistics; Matematisk statistik; matematisk statistik; Mathematical Statistics;

    Sammanfattning : In regression with near collinear explanatory variables, the least squares predictor has large variance. Ordinary least squares regression (OLSR) often leads to unrealistic regression coefficients. Several regularized regression methods have been proposed as alternatives. LÄS MER

  2. 2. Aspects of common principal components

    Författare :Toni Duras; Thomas Holgersson; Högskolan i Jönköping; []

    Sammanfattning : The focus of this thesis is the common principal component (CPC) model, the generalization of principal components to several populations. Common principal components refer to a group of multidimensional datasets such that their inner products share the same eigenvectors and are therefore simultaneously diagonalized by a common decorrelator matrix. LÄS MER

  3. 3. Bilinear Regression and Second Order Calibration

    Författare :Marie Linder; Pieter Kroonenberg; Stockholms universitet; []
    Nyckelord :NATURAL SCIENCES; NATURVETENSKAP; chemometrics; calibration; multivariate; hyphenated methods; matrix data; bilinear model; least squares; singular value decomposition; generalized rank annihilation; trilinear decomposition; parallel factor analysis; principal components regression; partial least squares; prediction; matematisk statistik; Mathematical Statistics;

    Sammanfattning : We consider calibration of second-order (or "hyphenated") instruments for chemical analysis. Many such instruments generate bilinear two-way (matrix) type data for each specimen. The bilinear regression model is to be estimated from a number of specimens of known composition. LÄS MER

  4. 4. Essays on Panel Data with Multidimensional Unobserved Heterogeneity

    Författare :Yana Petrova; Nationalekonomiska institutionen; []
    Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; Econometrics; Factor-Augmented Panel Regression; Interactive Effects; Unknown Factors; CCE Estimation; Principal Components;

    Sammanfattning : This thesis contributes to econometric methodology in terms of estimation and inference in static panel data models with unobserved multidimensional heterogeneity. When not properly accounted for, unobserved heterogeneity may introduce bias into the parameter estimates associated with covariates of interest, such as treatment indicators or determinants of macroeconomic indicators. LÄS MER

  5. 5. Statistical Methods for Designed Experiments and Spectroscopic Data

    Författare :Tobias Adolfsson; Göteborgs universitet; Göteborgs universitet; Gothenburg University; []
    Nyckelord :saturated orthogonal designs; multiple inference; step-down testing; partial least squares; principal components regression; multiple inference;

    Sammanfattning : This thesis consists of six papers related to saturated orthogonal designs, spectroscopic and high dimension data analysis. The first two papers deals with testing procedures for saturated orthogonal designs. Both the presented methods controls the multiple level of significance. LÄS MER