Logistic Liu Estimator under stochastic linear restrictions

Liu-Type logistic estimator under Stochastic Linear Restrictions

“…This example was used in Mansson et al(2012), Asar and Genc (2016), Wu and Asar (2016), and Varathan and Wijekoon (2016b) to illustrate results of their papers. The data consists the information about 100 municipalities of Sweden.…”

“…When the restrictions on the parameters are stochastic (third type), Nagarajah and Wijekoon (2015) introduced the new estimator called Stochastic Restricted Maximum Likelihood Estimator (SRMLE), and derived the superiority conditions of SRMLE over the LRE, LLE and RMLE. Also, by introducing the Stochastic Restricted Ridge Maximum Likelihood Estimator (SRRMLE) (Varathan and Wijekoon, 2016a), and the Stochastic Restricted Liu Maximum Likelihood Estimator (SRLMLE) (Varathan and Wijekoon, 2016b), the LRE and LLE estimators were further improved in the presence of stochastic restrictions. When comparing the above estimators, it can be noted that incoperating stochastic linear restrictions to the sample model (i.e.…”

“…When comparing the above estimators, it can be noted that incoperating stochastic linear restrictions to the sample model (i.e. the third type) improves the estimators further (Nagarajah and Wijekoon (2015), Varathan and Wijekoon (2016a), Varathan and Wijekoon, (2016b). This information motivated us to propose a new estimator under stochastic linear restrictions by considering to improve the performance of the logistic model.…”

On the stochastic restricted Liu-type maximum likelihood estimator in logistic regression model

“…We consider two criteria which are the simulated mean squared error (MSE) and predictive mean MSE (PMSE) to compare the performances of listed estimators. Following McDonald and Galarneau [10], and Varathan and Wijekoon [18], we generate the data matrix X containing the explanatory variables such that ρ 2 is the degree of collinearity between any two variables as follows:…”

“…In this article, we also suppose that β is subjected to lie in the sub-space restriction Hβ = h, where H is q × (p + 1) known matrix and h is a q × 1 vector of pre-specified values. This problem was also studied in Kibria and Saleh [7], Saleh and Kibria [6], Wu and Asar [15], Varathan and Wijekoon [16] For the stochastic linear restrictions, Varathan and Wijekoon [16], Varathan and Wijekoon [17] Varathan and Wijekoon [18] discussed the logistic regression model. In this paper, we will discuss the logistic regression model with stochastic linear restrictions.…”