Browsing by Author "Muhamad Safiih Lola"

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    Fuzzy parametric and fuzzy semi-parametric sample selection models
    ([Pulau Pinang]: Universiti Sains Malaysia, 2011) Muhamad Safiih Lola
    In the past thirty decade, regression model has received considerable attention and has been shown to be successful if applied together with other models. One of the most successful models is the sample selection model or the selectivity model. This model is a combination of the probit and regression models. The earlier studies on this model focused on the parametric approach.
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    The Performance of Double Bootstrap Method for Large Sampling Sequence
    (Open Journal of Statistics, 2016-10) Muhamad Safiih Lola; Nurul Hila Zainuddin
    Studies on the iteration procedure in double bootstrap method have given a great impact on confidence interval performance. However, the procedure was claimed to be complicated and demand intensive computer processor. Considering this problem, an alternative procedure was proposed in this research. Despite of using small sampling sequence, this research was aimed to increase the accuracy estimation using a second replication number which resulted in a large sampling sequence of double bootstrap. In this paper, the alternative double bootstrap method was hybrid onto an example model and its performance was based on Studentised interval. The performance was examined in simulation study and real sample data of sukuk Ijarah. The result showed that hybrid double bootstrap model gave more accurate estimation in terms of its shorter length when dealing with various parameter values and has shown to improve the single bootstrap estimation.
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    Sample Selection Model with Bootstrap (BPSSM) Approach:
    (Scientific Research Publishing, 2016) Muhamad Safiih Lola; Wan Saliha Wan Alwi; Nurul Hila Zainuddin
    Heckman Sampel Selection Model (PSSM) has been adopted widely in the study of labour work. This model contains exogenous, endogenous and standard error variables. However, this model is constantly exposed to high inaccuracy of estimation result. Therefore, to obtain an accurate and precise estimation, the bootstrap approach is introduced. The bootstrap approach will be hybrid with PSSM model known as BPSSM to achieve estimation result that is more precise. Then, the BPSSM is applied to Malaysian Population and Family Survey 1994 (MPFS-1994) data. The results showed that BPSSM provide a smaller standard error and shorter confidence intervals.