Maximum Likelihood Estimation for Censored Data

Maximum likelihood estimation for censored data is a fundamental statistical method applicable to various probability distributions. Key distributions include exponential distribution, Weibull distribution, log-normal distribution, and normal distribution. The exponential distribution, a continuous probability distribution commonly modeled using the `expfit` function in MATLAB, is typically employed to describe time intervals between events. The Weibull distribution, another continuous probability distribution often implemented via `wblfit`, is primarily used to model reliability and time-to-failure relationships. Log-normal distribution represents a probability distribution where the natural logarithm follows a normal distribution, frequently estimated using `lognfit` for parameter estimation. The normal distribution, a continuous probability distribution commonly handled with `normfit`, is widely applied to describe various variables in natural phenomena. Maximum likelihood estimation for these distributions typically involves optimizing likelihood functions using algorithms like Newton-Raphson or Expectation-Maximization (EM) to handle censored data points effectively.