Truncated Linear

Truncated linear methods address challenges arising when data is incomplete or subject to constraints, focusing on efficiently estimating linear models despite missing or selectively observed data points. Current research explores various algorithms, including stochastic gradient descent with kernel truncation for improved convergence and stability, and Markov Chain Monte Carlo methods like elliptical slice sampling for handling truncated multivariate normal distributions. These advancements are significant for improving the accuracy and efficiency of statistical inference in diverse applications, such as regression analysis with censored data, robust rotation search, and computed tomography reconstruction.