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Questions tagged [total-least-squares]

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.

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Variance Component Estimation of Weighted Total Least Squares

Note: I'd like to make the cited document accessible to everyone but I am unsure how to do this. I am a bit confused by this 2014 article "Variance components in errors-in-variables models: ...
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Derivative of Linear Model with respect to Residual

I am looking at two sections on the wikipedia page for total least squares, specifically: #Allowing_observation_errors_in_all_variables and #Example I have two questions, the first is how does one ...
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How to estimate a feasible Generalized Error-in-Variable Model (combine deming regression/TLS and f-generalized least squares)

I have observational data with spatial structure. A hypothetical dataset could be brain mass for 100 species of birds and body mass for those same species. The data has spatial structure because ...
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Is reduced major axis regression a special case of total least squares?

Edit: It seems the answer to my first question is that the website has a typo. $\lambda = V_{y}/V_{x}$, NOT $\lambda = V_{x}/V_{y}$. But I'm still stumped on the second question about why it cannot be ...
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Has the generalized Deming model been developed?

I am familiar with Deming regression and wonder if anyone has generalized it to other error families and links beyond Gaussian and identity.
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Comparing Deming/Orthogonal Regression to Null Hypothesis

I have some data of with the relationship Y=commonFactor+error1 and X=Alpha+Beta*commonFactor+error2 I want to test the hypothesis that Beta is non-zero, or that there is a significant relationship ...
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X and Y are correlated, errors in both X and Y but error variances unknown; How to predict X|Y or Y|X? Deming, bivariate gaussian ellipses, other?

Seeking relationships between two variable, both with random gaussian errors; ratio of error variances is unknown, no correlation of errors in X and Y, but another unknown variable Z (unmeasured) may ...
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Applications of total least squares and related pitfalls (in biology/ecology)

I recently discovered TLS for a bio-statistical (ecological) problem I am working on. However, I was not able to find a lot of (ecological) literature that uses TLS in their analysis. From other ...
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Mixed-level Deming regression?

Is there an implementation of Deming regression that also handles random intercepts and slopes in the sample? We have a situation where we have compelling theoretical reason to believe that one ...
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4 votes
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What does Deming regression estimate?

Least squares regression estimates conditional means. Least absolute regression estimates conditional medians. Quantile regressions estimate conditional quantiles (a special case of which is the ...
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What is the name of this regression model?

I am wondering how I can map this problem to something known. Let us start with a standard linear regression framework, and suppose we want to reconstruct an observed signal $y$ from single known ...
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Total least squares estimator

In the paper Ivan Markovsky, Sabine Van Huffel - Overview of Total Least Squares Methods, at the top of the sixth page, it is claimed that the estimate of $\beta$ in the total least squares model $y \...
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Reorder dataset to achive LSE between two data sets

Assume I have two datasets, each one containing 5000 samples, and each sample has three dimensions. I am looking for a way to "reorder" the samples in one (or probably both) dataset such ...
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Orthogonal regression that minimizes absolute error instead of squared error

Fitting a line through 3D points is usually done by orthogonal least squares (aka "total least squares"), i.e., by minimizing the mean squared orthogonal distance between the points and the ...
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duel regression with orthogonal constraints on coefficients [closed]

I'm trying to solve a problem where I need to find two deming style regression models onto two different data sets of equal dimension who's coefficients(A,B) satisfy the following criteria with ...
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Propagating uncertainty in least squares regression

I am currently running a nonlinear regression on $y=f(x,\theta)$, the variable $x$ being a known input and $y$ being a measurement result. I would like to estimate the parameters $\theta$ as well as ...
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How to handle nondifferentiable points of the objective function in the geometric circle fit?

We a given measured points in $(x_i,y_i) \in \mathbb{R}^2$, $i=1,..,n$. The geometric circle fit is the circle with center (a,b) and radius $r$ that minimizes the squared Euclidean distances between ...
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Errors only in variables model, and polynomial fitting

I have a bunch of data points $(x, y)$, and I know that they fit well to a model of the form $y = a + bx + c x^2$, with $a \approx 0.01, \ b \approx 1\ \textrm{and}\ c \lesssim 0.1$. I'd like to fit ...
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Use of ordinary least squares line in correlation analyses

I want to study and plot the correlation between two variables, X,Y. Both are measured, so they have comparable noise. Correlation (and not regression) is the correct analysis here. In a paper, I ...
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How to do Error in Variables regression with known standard errors

I need some help with EiV regression and comparison of two methods. I have used two different methods to estimate the size of the same population and would like to find out how good method 1 is ...
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Linear regression with normally distributed data and model with arbitrary covariance

Consider the linear regression problem $$(A+\Delta A)x = b + \Delta b.$$ If $\Delta A = 0$ and $\Delta b$ is identically and independently distributed, then ordinary least-squares gives a good (BLUE) ...
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Which is more correct to calculate least squares (errors) during nonlinear curve fitting, calculating after transformation or in the original form?

I am studying curve fitting and linear regression. I am supposed to find a and b in the equation $$ P=ae^{bh} $$ so I transformed it to $$ lnP=ln a +bh $$ then $$ Y=c+bX $$ after that I solved it to ...
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Identifying an algorithm described as 'Tukey approach' for ignoring outliers?

MVTec's machine vision library Halcon has an operator fit_line_contour_xld for robustly fitting lines to 2D points. Here's the documentation entry for that operator:...
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Including model uncertainty in non-linear least squares minimization

The problem I have experimental data $Y$ with heteroscedastic and normally distributed uncertainties characterized by covariance matrix $C_{exp}$. I want to fit the data using model $F(X, \beta)$ ...
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Standardizing regression coefficients in the case of Total Least Squares Regression

So I know that there exits a method to standardize regression coefficients by simply multiplying them with the ratio of the standard deviations of the two variables. I'm wondering if this holds true ...
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Difference between estimating parameters for prediction and estimating parameters for their own sake

In a 1989 paper on orthogonal regression, Ammann and Van Ness write: An important caveat should be noted. The errors-variables-model is useful when the primary goal is to estimate the model ...
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Difference between "orthogonal distance regression" and "total least squares"

I'm trying to figure out the difference(s) between total least squares (TLS) and orthogonal distance regression (ODR). Both techniques are used when there is error in the dependent variable. Per this ...
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least absolute deviation version of deming [closed]

Based on what I know, deming minimizes the sum of square of perpendicular distance to the regression line. Is there a package in R that can run regression that minimize the sum of absolute value of ...
CABLE's user avatar
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Why my Deming Regression line change so much when switching variables? If they seem to be a linear relationship betwen them?

I am trying to fit a line that best predicts the production of energy Y given the speed of wind X, a typical Y = xm + b , using deming regression. I am looking for the slope and the intercept of that ...
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Correct way to fit a line in 3D (x-position vs y-position vs other quantity)

I have measured the position of light spots $(x,y)$ in an arbitrarily chosen basis and I compare that to some other measured quantity, say the brightness of each spot $B$. Now, in theory all the ...
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Deming regression prediction interval using jackknife resampling

I am trying to write a custom Deming function following the maths in Linnet (1993): https://www.ncbi.nlm.nih.gov/pubmed/2281234 Using jackknife resampling I calculate the standard error for the ...
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1 answer
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Standard error of coefficient estimates for model II regression

I'm working with time series data that has error in both the dependent and independent variables, so I'm analyzing each half hour of data with model II linear regression, specifically geometric mean ...
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1 answer
799 views

Selecting appropriate likelihood during non-linear regression

When performing regression to fit a function, $f (x,{\bf \beta})$, to a set of observed data, $y_i(x_i)$, we are seeking to optimize the parameters, $\beta$, of the fitting function, to minimize some ...
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Can total least squares be used to account for uncertainty due to measurement timing?

Suppose we have a dataset in which we wish to perform regression analysis, and where the response/dependent variable is a measurement at time T. But due to pragmatic sampling we do not have ...
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Regression to estimate parameters

I would like some suggestions to tackle the following problem. Given a system $y = X\beta$ where $y \in \mathcal{R}^m$, $X \in \mathcal{R}^{m \times n}$, $m\geq n$, and $\beta \in \mathcal{R}^n$, ...
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1 answer
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Orthogonal polynomials + cross validation: should subsetting be done prior or after constructing the orthogonal polynomials?

So, just to start... I've just learned of orthogonal polynomial regression today. I've gone through the master's-level linear models courses, and we did not cover that topic. I was always under the ...
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Why does the total least squares line in 2D pass through the average across all data points?

I have $N$ data points $\mathbf{m_k}$ and I want to fit a line through them with minimal error $$J = \sum_k^N ||\mathbf{m_k^*} - \mathbf{m_k}||^2 = \sum_k^N ||\mathbf{m_0} + a_k\mathbf{e} - \mathbf{...
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Advantage of orthogonal polynomials

What is the sense or background of orthogonal polynomials (regarding using mixed models)? I would like to know why they shall or should be orthogonal. Is it to build independent sample points? On Is ...
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Least Squares Derivation

I come from physics and would like to derive the chi-square function given by the Particle Data Group: \begin{equation} \chi^2 (\boldsymbol\theta) = (\boldsymbol y-\boldsymbol\mu(\boldsymbol \...
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1 answer
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Deming regression implementation: force intercept to 0

I implemented Deming Regression in a known programming language, using the algorithm from here: https://en.wikipedia.org/wiki/Deming_regression However, the algo does not specify what to do in case we ...
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What's the shape of confidence interval for linear regression estimated through TLS (total least squares)?

So, we know the shape of confidence intervals for vanilla linear regression estimated through OLS (ordinary least squares): Shape of confidence interval for predicted values in linear regression. In ...
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When to use Deming regression

I am currently working on a way to transform two different phosphorus test values into each other. Background There exist many (extraction) methods to measure plant available phosphorus in soil. ...
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1 answer
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Generating Random Data Sets for Linear Regression with Random Slope and Error Term in R

I want to test the effects of sample size on Deming regression using simulated paired data in R. As the data are paired, the expected slope value should be 1 and the intercept 0. The code I have ...
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1 answer
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Specifying the Error Ratio in Deming Regression

We use Deming Regression for method validation in our clinical ELISA laboratory. As we run clinical trials this needs to be performed if we get a new reagent batch, batch of plates, new plate reader ...
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Regression of dataset with many x values per y value

I am performing a total least squares regression in which I have many x observations for a given y observation. The x observations are normally distributed. I am aware I could do some sort of ...
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2 answers
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Find best fit diagonal matrix for error minimization

I want a set of input values to be as similar to the output values as possible. I have an input matrix X (m*n) that has m data points and n dimensions for each data point. I also have an output matrix ...
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What weights for weighted Total Least Square (TLS) regression?

I have a dataset with known errors in both the X and Y and want to perform a simple linear regression. From reading other posts, it seems I want to TLS over OLS due to the presence of error in both ...
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Multiple errors-in-variables regression with collinearities

I have a $[k \times N]$ matrix of predictors / independent variables and a $[k \times N]$ matrix of predictands / dependent variables. I have uncertainty estimates for each predictor and each ...
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$X$ and $Y$ at different scales in total least squares?

As this section of the Wiki article says, the best (in total least squares sense) matrix $B$ that projects $X$ to $Y$ is given by $$B=-V_{XY}V_{YY}^{-1},$$ where $V_{XY}$ and $V_{YY}$ come from the ...
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1 answer
147 views

Is quantile regression a better option than total least squares RMA in this case

I have paired cobalt concentrations in bird blood and feathers. Blood levels give me an idea of how recent the cobalt exposure was (<30days), feather give the 6 month accumulated total. Previous ...
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