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The Pakistani Federation has vested more autonomy to the provinces through the 18th constitutional amendment. This autonomy has evidenced impetus for the continuing demands of sub-national sets and provincial minorities for the creation of new provinces in Pakistan. The identity crisis and politics of ethnicity have been reawakened after the 18th constitutional amendment. The Hazarawal (variousethnolinguistic groups inhabited in Hazara) of Khyber Pakhtunkhwa Province have been striving for a separate province since the 1950s, andthe voice for separate identity re-emerged as a full-fledged movement after the 18th amendment. This analytical study proposes that the movement seems quite fruitful in attaining some of its essential objectives.Nevertheless, the movement is still far away from achieving its prime and chief political terminus, i.e., the creation of a separate Hazara province.In this context, this paper tries to explore the identity crisis and politics of ethnicity in the Hazara region. The paper contends that without adopting some more effective and substantial measures, the movement could not attain its goalmouth in the near future. However, a more flexible, inclusive and accommodate approach would boost the chance of its ultimate success.

This paper considers the concomitant based double ranked set sampling (CDRSS) for estimating the population proportion and compares with existing concomitant based ranked set sampling (CRSS) and si...

This paper considers the concomitant-based rank set sampling (CRSS) for estimation of the sensitive proportion. It is shown that CRSS procedure provides an unbiased estimator of the population sensitive proportion, and it is always more precise than corresponding sample sensitive proportion (Warner SL (1965)) that based on simple random sampling (SRS) without increasing sampling cost. Additionally, a new estimator based on ratio method is introduced using CRSS protocol, preserving the respondent’s confidentiality through a randomizing device. The numerical results of these estimators are obtained by using numerical integration technique. An application to real data is also given to support the methods.

Cost-effective and efficient sampling methods are of main concern in many social, biological and environmental studies. In this article, an efficient sampling scheme, named manipulation-based ranked set sampling (MBRSS) scheme is introduced with its properties for estimating population mean and median. The MBRSS is a mixture of simple random sampling (SRS), ranked set sampling (RSS) and median ranked set sampling (MRSS) schemes and is applicable in the situation when ordinary RSS cannot be conducted. It is shown that the proposed scheme provides unbiased mean estimator provided underlying distribution is symmetric. For asymmetric distributions, a weighted mean is proposed, where optimal weights are computed using Shannon's entropy. Monte Carlo simulation is used to ascertain effectiveness of the proposed mean and median estimators in the presence of outliers. We also compared the efficiency of MBRSS and truncation-based ranked set sampling (TBRSS) scheme with respect to SRS under the situation of perfect and imperfect ranking i.e error in rankings with respect to variable of interest. It is observed, on the basis of theoretical and numerical studies that MBRSS is more efficient than SRS. Further, a real data set is used to illustrate the proposed MBRSS scheme.

In this paper, we propose a double robust truncation based ranked set sampling (DRTBRSS) for estimating population mean and median.It provides unbiased estimator of population mean under symmetric population and its efficiency dominates the efficiency of conventional estimators based on ranked set sampling (RSS), truncation based rank set sampling (TBRSS) and simple random sampling (SRS). Monte Carlo simulation, based on perfect ranking and ranking based on auxiliary variable, is used under symmetric and asymmetric distributions to evaluate effectiveness of the proposed sampling method. Further, a real data set is used to illustrate the proposed sampling design.