maximum likelihood

Flash floods are becoming a critical issue as they occur more frequently in recent years.  Managing watersheds and water resources, researching floods and droughts, and monitoring climate change are all connected to annual precipitation.  Therefore, discovering the most accurate method for calculating annual precipitation is crucial.  This study compares two basic approaches to estimating annual precipitation parameters: parametric and nonparametric.  The research focuses on fitting the distribution of annual precipitation for fifteen strategically located rain gauge st

In this work, we propose a new estimate algorithm for the parameters of a $\mathrm{GARCH}(p,q)$ model.  This algorithm turns out to be very reliable in estimating the true parameter’s values of a given model.  It combines maximum likelihood method, Kalman filter algorithm and the simulated annealing (SA) method, without any assumptions about initial values.  Simulation results demonstrate that the algorithm is liable and promising.