Error In Measurement Model Nonlinear
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What Is Non Linear Measurement
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von GoogleAnmeldenAusgeblendete FelderBooksbooks.google.de - This monograph provides an up-to-date discussion of analysis strategies for regression problems in which predictor variables are measured with errors. The analysis of nonlinear regression models includes generalized linear models, transform-both-sides models and quasilikelihood and variance function...https://books.google.de/books/about/Measurement_Error_in_Nonlinear_Models.html?hl=de&id=FS-x3tPdXeMC&utm_source=gb-gplus-shareMeasurement Error in Nonlinear ModelsMeine BücherHilfeErweiterte BuchsucheE-Book anzeigenNach Druckexemplar suchenCRC PressAmazon.deBuch.deBuchkatalog.deLibri.deWeltbild.deAlle Händler»Measurement Error in Nonlinear ModelsRaymond J. Carroll, David Ruppert, Leonard A. StefanskiCRC Press, 06.07.1995 - 336 Seiten 0 Rezensionenhttps://books.google.de/books/about/Measurement_Error_in_Nonlinear_Models.html?hl=de&id=FS-x3tPdXeMCThis monograph provides an up-to-date discussion https://www.amazon.com/Measurement-Error-Nonlinear-Models-Perspective/dp/1584886331 of analysis strategies for regression problems in which predictor variables are measured with errors. The analysis of nonlinear regression models includes generalized linear models, transform-both-sides models and quasilikelihood and variance function problems. The text concentrates on the general ideas and strategies of estimation and inference rather than being concerned https://books.google.com/books?id=FS-x3tPdXeMC with a specific problem. Measurement error occurs in many fields, such as biometry, epidemiology and economics. In particular, the book contains a large number of epidemiological examples. An outline of strategies for handling progressively more difficult problems is also provided. Voransicht des Buches » Was andere dazu sagen-Rezension schreibenEs wurden keine Rezensionen gefunden.Ausgewählte SeitenTitelseiteInhaltsverzeichnisIndexVerweiseInhaltREGRESSION AND ATTENUATION 21 Error Structure 23 REGRESSION CALIBRATION 40 SIMULATION EXTRAPOLATION 79 INSTRUMENTAL VARIABLES 107 FUNCTIONAL METHODS 122 LIKELIHOOD AND QUASILIKELIHOOD 141 BAYESIAN METHODS 165 UNKNOWN LINK FUNCTIONS 199 HYPOTHESIS TESTING 206 DENSITY ESTIMATION AND NONPARAMET 215 RESPONSE VARIABLE ERROR 229 OTHER TOPICS 243 A FITTING METHODS AND MODELS 257 References 280 Author index 298 MehrSEMIPARAMETRIC METHODS 182 Urheberrecht WenigerAndere Ausgaben - Alle anzeigenMeasurement Error in Nonlinear Models: A Modern Perspective, Second EditionRaymond J. Carroll,David Ruppert,Leonard A. Stefanski,Ciprian M. CrainiceanuEingeschränkte Leseprobe - 2006Measurement Error in Nonlinear Models: A Modern PerspectiveRaymond J. CarrollKeine
linear model Generalized linear model Discrete choice Logistic regression Multinomial logit Mixed logit Probit Multinomial probit Ordered logit Ordered https://en.wikipedia.org/wiki/Errors-in-variables_models probit Poisson Multilevel model Fixed effects Random effects Mixed model Nonlinear regression Nonparametric Semiparametric Robust Quantile Isotonic Principal components Least angle Local Segmented Errors-in-variables Estimation Least squares Ordinary least squares Linear (math) Partial Total Generalized Weighted Non-linear Non-negative Iteratively reweighted Ridge regression Least absolute deviations Bayesian Bayesian multivariate error in Background Regression model validation Mean and predicted response Errors and residuals Goodness of fit Studentized residual Gauss–Markov theorem Statistics portal v t e In statistics, errors-in-variables models or measurement error models[1][2] are regression models that account for measurement errors in the independent variables. In contrast, standard regression models measurement error in assume that those regressors have been measured exactly, or observed without error; as such, those models account only for errors in the dependent variables, or responses.[citation needed] In the case when some regressors have been measured with errors, estimation based on the standard assumption leads to inconsistent estimates, meaning that the parameter estimates do not tend to the true values even in very large samples. For simple linear regression the effect is an underestimate of the coefficient, known as the attenuation bias. In non-linear models the direction of the bias is likely to be more complicated.[3][4] Contents 1 Motivational example 2 Specification 2.1 Terminology and assumptions 3 Linear model 3.1 Simple linear model 3.2 Multivariable linear model 4 Non-linear models 4.1 Instrumental variables methods 4.2 Repeated observations 5 References 6 Further reading 7 External links Motivational example[edit] Consider a simple linear regr