Description: Quasi-maximum likelihood is an estimation method used in statistics that is based on maximizing a quasi-likelihood function. This approach is particularly useful in statistical models where the complete likelihood function may be difficult to specify or compute. Unlike traditional maximum likelihood, which requires a precise specification of the probabilistic model, quasi-likelihood allows for a more general form of the likelihood function, facilitating estimation in situations where classical assumptions do not hold. This method is commonly used in a variety of statistical models, including regression models, time series analysis, and modeling count data, among others. Quasi-maximum likelihood provides estimates that are consistent and asymptotically normal under certain conditions, making it a valuable tool for statisticians and data analysts. Additionally, its flexibility allows it to address a wide range of problems where standard models may not be applicable, making it relevant in various areas of research and practical application.
