EXPLORATION OF DISTRIBUTIONâS PARAMETERS AND CONFIDENCE INTERVAL - Ismail Walid, M.
The pattern formed from a risk data can be studied and represented become a certain distribution. Each distribution has a component that can affect the formed pattern, namely parameters. Parameter is a value that can describe the population. The unknown parameter can be estimated by parameter estimation which can be either a point estimation or an interval estimation. The method that can be used for point estimation is the maximum likelihood estimation method and the Bayesian estimation method. The maximum likelihood estimation method can be used for several distribution parameters such as the ?, ?2, p, or the parameters for the aggregate risk models. Meanwhile, the Bayesian estimation method is used to estimate the parameters treated as random variables. The point estimation is considered to be less representative of the parameter value, because it is only a single value, so an interval estimation is required. One of the more popularly interval estimation is the confidence interval. The confidence interval of a parameter is the point estimate value added (subtracted) by a statistic which multiplied by the standard error. In addition to single risk data, parameter estimation can also be determined for paired (bivariate) risk data. For bivariate paired data from different distributions, bivariate Copula can be used as an alternative to obtain a joint distribution function. From the Copula, the parameter values can also be estimated using the maximum likelihood estimation method. From the point estimation for the Copula parameter, the confidence interval can be determined. The criteria for a good confidence interval can be seen from the length of the interval and its probability (level of confidence).