The British Colonial Encounter with the Pukhtuns: An Appraisal of Faqir Ippi’s Struggle against the British Raj (1936-1947)

The North-West Frontier region of the British Empire in India during the Great Game was part of the ‘Ring Fence Strategy’, framed by the Raj against its adversaries and rivals in Central and South Asia. To protect her ‘Jewel in the Crown’- India, the British Raj made several moves in the strategically placed Pukhtun1 land. The Pukhtun populace, adherent to their centuries old code of conduct, Puḳhtūnwali, consistently resisted the British encroachment of their territory. Mirza Ali Khan, popularly known as Faqir Ippi, was one of the many freedom fighters who challenged the imperialist power in this region. Taking notice of Islam Bibi’s case, a Hindu Convert, Faqir Ippi mobilized the Pukhtuns of Waziristan in defying and fighting the British. He was a serious contestant to the British authority with his well-known fighting skills, effective planning and guerilla tactics in one of the most difficult terrains. The entire Tribal Belt, especially Waziristan, proved to be a ‘turbulent frontier’ for nearly eleven years, i.e. 1936-1947. This insurgency started bringing bad name to the crown and encouraging others to rise against the British. To contain and end Faqir Ippi’s resistance, Governor George Cunningham hired the locals to instigate and bribe his followers to rise and fight against him. The aim of this paper is a critical evaluation of the British strategy in this region and an appraisal of Faqir Ippi’s response and assessment of how successful he was in invigorating Pukhtun resistance to defend their motherland, using both colonial and local sources.

Robust Outlier Detection Techniques for Skewed Distributions and Applications to Real Data

Most of the data sets belonging to the real world contain observations at the extremes that might not be in conformity with the remaining data set. These extreme observations known to be outliers might have positive or negative effect on the data analysis like regression estimates, forecasting and ANOVA etc. Outliers are powerful tools to identify the most interesting events of the world in cross sectional data and historically important events can be picked by detecting outliers in time series data sets. Numerous outlier detection techniques have been proposed in the literature. This study provides a survey of these techniques and their properties. Most of these techniques work well under the assumption that data come from a symmetric distribution and these techniques fail to work in skewed distributions. Because of this limitation, Hubert and Vandervieren (2008) proposed a technique for outlier‟s detection in skewed data sets. Our thesis presents a new technique to measure robust skewness (SSS) and a new outlier detection technique (SSSBB) for skewed data distributions. The study shows that the proposed technique measures skewness more accurately than existing techniques and the proposed technique for outlier‟s detections works better than Hubert‟s technique on a class of theoretically skewed and symmetric distributions. The study also compares the technique with other established outlier detection techniques in the literature. This study uses simulation technique for computer generated distributions and some real data sets for comparison purposes. The study also analyzes real life data sets and compares the baby birth weight data and stock returns, both of which are known to be skewed. These results will help us in making a choice of appropriate outlier detection technique for skewed data sets for different sample sizes which might be helpful in identifying underweight babies.