A technique to estimate parameters $\beta$ of the linear model $Y=X\beta$ when both $Y$ and $X$ are subject to measurement error. Includes Orthogonal and Deming regression as special cases.
TLS is a technique to estimate parameters $\beta$ of the linear model $Y=X\beta$ when both $Y$ and $X$ are subject to measurement error. This is in contrast with ordinary least squares (OLS) regression where $X$ is assumed to be known exactly. Some special cases of TLS are known as orthogonal regression (single $x$ and single $y$) and Deming regression (single $x$ and $y$ but with different error variances; it is an example of weighted TLS).
