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آنکھ سے دور سہی دل کے قریں رہنے دے

آنکھ سے دُور سہی، دل کے قریں رہنے دے
میری ہر سانس میں تو خود کو مکیں رہنے دے

میں کہ اک عکس ہوں گمنام سا پس منظر ہوں
کب کہاں کیسے کسی طور کہیں رہنے دے

اک نظر مجھ پہ مرے ماہِ منیر ایسی ہو
کب طلب میں نے کیا زر یا نگیں، رہنے دے

میں ہوں اس قافلۂ عشق سے بچھڑا راہی
میرا کب ٹھور ٹھکانہ ہے کہیں، رہنے دے

تجھ سے منسوب ہوئی، تجھ سے ہی منسوب رہوں
غیر کے آگے جھکے گی یہ جبیں، رہنے دے

دل میں یا آنکھ میں یا دستِ حنائی میں فضاؔ
تیری مرضی ہے جہاں چاہے، وہیں رہنے دے

Exploring Dialogism in the Mistress-Slave Relationship: A Study of Female Slave Characters in Jean Rhys' “Wide Sargasso Sea”

Slavery, an enduring institution devoid of remuneration, has played a foundational role in numerous societies. Literary works explore the changing roles and portrayals of slaves in Great Britain's post-emancipation era in 1833. Within Jean Rhys' seminal work, "Wide Sargasso Sea," a captivating narrative emerges, spotlighting a female slave character whose mistress forms a profound, almost maternal, attachment. This study embarks on an exploration of this intricate mistress-slave dynamic, particularly focusing on the slave's portrayal in a maternal capacity. It delves into the question whether a slave could embody the utmost empathy towards the extended familial network of her mistress. Framing this investigation is Mikhail Mikhailovich Bakhtin's theory of dialogism, asserting that interaction is shaped by discourse and that each dialogue carries profound significance. The poignant instance of the slave providing solace as Antoinette's mother falters exemplifies this theory. The findings substantiate the hypothesis that the language employed in reciprocal communication profoundly impacts the tenor of the relationship. This study thus sheds light on the profound interplay between language, empathy, and power dynamics within the

Improved Inference under Heteroscedasticity of Unknown Form Using a New Class of Bootstrap and Nonparametric Estimators

It is well-known that use of ordinary least squares for estimation of linear regression model with heteroscedastic errors, always results into inefficient estimates of the parameters. Additionally, the consequence that attracts the serious attention of the researchers is the inconsistency of the usual covariance matrix estimator that, in turn, results in inaccurate inferences. The test statistics based on such covariance estimates are usually too liberal i.e., they tend to over-reject the true null hypothesis. To overcome such size distortion, White (1980) proposes a heteroscedasticity consistent covariance matrix estimator (HCCME) that is known as HC0 in literature. Then MacKinnon and White (1985) improve this estimator for small samples by presenting three more variants, HC1, HC2 and HC3. Additionally, in the presence of influential observations, Cribari-Neto (2004) presents HC4. An extensive available literature advocates the use of HCCME when the problem of heteroscedasticity of unknown from is faced. Parallel to HCCME, the use of bootstrap estimator, namely wild bootstrap estimator is also common to improve the inferences in the presence of heteroscedasticity of unknown form. The present work addresses the same issue of inference for linear heteroscedastic models using a class of improved consistent covariance estimators, including nonparametric and bootstrap estimators. To draw improved inference, we propose adaptive nonparametric versions of HCCME, bias-corrected versions of nonparametric HCCME, adaptive wild bootstrap estimators and weighted version of HCCME using some adaptive estimator, already available in literature, namely, proposed by Carroll (1982). The performance of all the estimators is evaluated by bias, mean square error (MSE), null rejection rate (NRR) and power of test after conducting extensive Monte Carlo simulations.
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