# Computationally and statistically efficient truncated regression

• ## What is an example of a truncated model?

A sample is truncated if some observations are systematically excluded from the sample.
For example, suppose we are interested in relationship between people's income(y) and education(x).
If we have observations of both y and x only for people whose income is above \$20,000 per year, then we have a trun- cated sample..

• ## What is the difference between truncated regression and censored regression?

Truncation and censoring
Truncation is a type of missing data problem where you are simply unaware of any data where the outcome variable falls outside of a certain set of bounds.
Censoring occurs when a measurement has a sensitivity with a certain set of bounds..

• ## What is the meaning of truncated regression?

The truncated regression model is defined by observations of1 (Y,X) generated by.
Y = Xβ + U. conditional on Y \x26gt; 0.
In other words, it is a linear regression model with a sampling plan that samples (Y,X) from the conditional distribution of (Y,X) given Y \x26gt; 0..

• Truncated data represent values that couldn't have been observed, overall or for a particular individual.
Your analysis then must take into account that you have no information about observations that might fall into that range.
We provide a computationally and statistically efficient estimator for the classical problem of trun- cated linear regression, where the dependent variable y = wTx+ε and its corresponding vector of covariates x ∈ Rk are only revealed if the dependent variable falls in some subset S ⊆ R; otherwise the existence of the

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