A Qualitative Study of Women Domestic Workers using Public Transport and the Adoption of Purdah

The aim of this research is to analyze the key issues concerning the mobility of women domestic workers working in Lahore, Pakistan, through a qualitative feminist approach. For data collection, the walking interview method, which entailed walking alongside the participants and asking them questions regarding the study questions. Qualitative thematic analysis was used to analyze the interviews with ten participants. The findings reveal that the key deterrent limiting women’s mobility includes the extremely patriarchal socio-cultural norms that surround women’s lives. Two broad themes were found including: (i) Purdah as an Extension of One’s Agency; and (ii) Personal Safety, Cost, and Overcrowded Public Transport. This paper changes the societal implications of purdah while also shifting the narrative that surrounds it through the way women participants use purdah to extend their very limited agency. The study also helps to shed light on Pakistan’s public transit system through the eyes of women domestic workers who view it as a deeply unsafe and uncomfortable mode of traveling within the city.

New Diagnostic Methods for Influential Observations in Linear Regression With Some Biased Estimation Methods

This thesis is concerned with the expansion of diagnostic methods in parametric regression models with some biased estimators. Of which, the Liu estimator, modified ridge estimator, improved Liu estimator and ridge estimator have been developed as an alternative to the ordinary least squares estimator in the presence of multicollinearity in linear regression models. Firstly, we introduce a type of Pena’s statistic for each point in Liu regression. Using the forecast change property, we simplify the Pena’s statistic in a numerical sense. It is found that the simplified Pena’s statistic behaves quite well as far as detection of influential observations is concerned. We express Pena’s statistic in terms of the Liu leverages and residuals. For numerical evaluation, simulated studies are given and a real data set has been analyzed for illustration. Secondly, we formulated Pena’s statistic for each point while considering the modified ridge regression estimator. Using this statistic, we showed that when modified ridge regression was used to mitigate the effects of multicollinearity, the influence of some observations could be significantly changed. The normality of this statistic was also discussed and it was proved that it could detect a subset of high modified ridge leverage outliers. The Monte Carlo simulations were used for empirical results and an example of real data was presented for illustration. Next, we introduce a type of Pena’s statistic for each point in the improved Liu estimator. Using this statistic, we showed that when the improved Liu estimator was used to mitigate the effects of multicollinearity, the influence of some observations could be significantly changed. The Monte Carlo simulations were used for empirical results and an example of real data was presented for illustration. The ridge estimator having growing and wider applications in statistical data analysis as an alternative technique to the ordinary least squares estimator to combat multicollinearity in linear regression models. In regression diagnostics, a large number of influence diagnostic methods based on numerous statistical tools have been discussed. Finally, we focus on ridge version of Nurunnabi et al. (2011) method for identification of multiple influential observation in linear regression. The efficiency of the proposed method is presented through several well-known data sets, an artificial large data with high-dimension and heterogeneous sample and a Monte Carlo simulation study.