ESTIMASI PARAMETER MODEL REGRESI LOGISTIK MENGGUNAKAN METODE JACKKNIFE - , HANA FITIANINGRUM ([Yogyakarta] : Universitas Gadjah Mada, 2013)
Jackknife is one of the estimation methods, computer-based statistical inference. Its working principle is using a computer in generating original data from a small sample to get an pseudo sample. Pseudo sample is obtained by removing an observation from the original sample can then be used to calculate the value of the estimator. One of the jackknife methodâs advantage is no need of any assumptions regarding the distribution of the sample possessed. The main purpose of this method is to obtain the best possible estimate based on minimal data with the help of computers. A jackknife method can be used on paired data for purposes ratio and in the case regression models. In this paper, a jackknife method is applied to estimate the parameters of a logistic regression model. Logistic regression model is a form of regression analysis to determine a causal relationship (causality) when the response variable Y has only two possible values / results or data are dichotomous. The method which is often used to solve the logistic regression problem is Maximum Likelihood Estimation (MLE) where the parameter estimation process is preceded by the formation of likelihood function. Jackknife method in estimating parameters of the logistic regression model is illustrated in the determination of the level of bankruptcies in the Indonesian banking firm selected randomly. Based on the results of the analysis, jackknife method is able to reduce the standard errors to jackknife deleted-2.