A Comparison of Ordinary Least Squares and Least Absolute Error Estimation

Least Absolute Relative Error Estimation.

Multiplicative regression model or accelerated failure time model, which becomes linear regression model after logarithmic transformation, is useful in analyzing data with positive responses, such as stock prices or life times, that are particularly common in economic/financial or biomedical studies. Least squares or least absolute deviation are among the most widely used criterions in statisti...

A Comparison of Ordinary Least Squares and Logistic Regression

Regression Model Estimation Using Least Absolute Deviations , Least Squares Deviations and Minimax Absolute Deviations Criteria

Regression models and their statistical analyses is the most important tool used by scientists in data analyses especially for modeling the relationship among random variables and making predictions with higher accuracy. A fundamental problem in the theory of errors, which has drawn attention of leading mathematicians and scientists since past few centuries, was that of fitting functions. For t...

Differentially Private Ordinary Least Squares

Linear regression is one of the most prevalent techniques in machine learning; however, it is also common to use linear regression for its explanatory capabilities rather than label prediction. Ordinary Least Squares (OLS) is often used in statistics to establish a correlation between an attribute (e.g. gender) and a label (e.g. income) in the presence of other (potentially correlated) features...

Differentially Private Ordinary Least Squares

More specifically, we use Theorem B.1 from (Sheffet, 2015) that states that given a matrix A whose all of its singular values at greater than T ( , δ) where T ( , δ) = 2B (√ 2r ln(4/δ) + 2 ln(4/δ) ) , publishing RA is ( , δ)differentially private for a r-row matrix R whose entries sampled are i.i.d normal Gaussians. Since we have that all of the singular values of A′ are greater than w (as spec...