Errors In Variables

Article Index for Errors

Website Links For Model

Information About Errors In Variables

	CATEGORIES ABOUT ERRORS-IN-VARIABLES MODEL
	statistics

	APPAREL
	BABY
	BEAUTY
	BOOKS
	CAR TOYS
	CELL PHONES
	DVD'S
	ELECTRONICS
	GOURMET FOOD
	GROCERIES
	HEALTH & PERSONAL
	HOME & GARDEN
	JEWELRY
	MUSIC
	MUSIC INSTRUMENTS
	OFFICE PRODUCTS
	SOFTWARE
	SPORTING GOODS
	TOOLS & HARDWARE
	TOYS
	VIDEO GAMES
	SHOPPING HOME
	MORE SHOPPING...

ROBUST LINEAR REGRESSION In Linear Regression , the Least Squares (LS) attributes all Error to the dependent variables. It has variant versions according to other error configurations such as total least squares (i.e. orthogonal error), data least squares (DLS), constrained or structured TLS and so on. Given an observation vector $\backslash mathbf\{b\}\; \backslash in\; eals^n$ and a data Matrix $\backslash mathbf\{A\}\; \backslash in\; eals^\{n\; imes\; m\}$, consider the solution of the overdetermined system of equations $\backslash mathbf\{Ax\; \backslash approx\; b\}$.

  Where <math>{\cdot} F</math> Is The "http://wwwinformationdelightinfo/encyclopedia/entry/Frobenius_norm" class="copylinks">Frobenius Norm (or in human English: the "length" of the vector) and the perturbations <math>\Delta\mathbf{A}</math> and <math>\Delta\mathbf{b}</math> are used to compensate for the noisy signals <math>\mathbf{A}</math> and <math>\mathbf{b}</math>, respectively This formulation of TLS also implies that the noises are assumed to be independently, identically distributed (<i>iid</i>) both in <math>\mathbf{A}</math> and <math>\mathbf{b}</math> Note that the objective can have a weighting matrix according to the distribution of errors if the distribution is known or well-estimated, which is called the constrained or structured TLS