What does LTS mean in MATHEMATICS

LTS stands for Least Trimmed Squares which is an alternative method for fitting a model to data

Ordinary least-squares regression seeks to minimize the sum of squared residuals (the difference between observed values and predicted values). In contrast, LTS seeks to minimize a trimmed version of this sum that excludes some of the data points closest to the fitted line or curve

The main benefit of using LTS instead of ordinary least-squares regression is its robustness in the face of outliers or noisy data. By excluding the closest points to the fitted line/curve, it can improve the accuracy and reliability of your models even when there are unexpected or extreme values

The exact number and identity of data points excluded from an LTS regression depends on a variety of factors, including how much trimming has been done, how many parameters are being estimated, and how much noise is in your dataset. Generally speaking, however, more trimming means fewer points excluded from your eventual model

There are many other optimization techniques that can be used instead of LTS for fitting models to datasets. Some popular alternatives include ordinary least-squares regression, ridge regression, lasso regression and gradient boosting regressions. Each technique has its own pros and cons which need to be considered depending on the size and nature og your dataset