LINEAR REGRESSION WITH DATA MISSING NOT AT RANDOM: BOOTSTRAP APPROACH

Rakhimov, Zarrukh, Rahimova, Nilufar, Рахимов, Заррух, Рахимова, Нилуфар, Рахимов, Заррух, Рахимова, Нилуфар

Иқтисодий тараққиёт ва таҳлил · 2024-yil

Annotatsiya

OLS regressions have a set of assumption in order to have its point and interval estimates to be unbiased and efficient. Data missing not at random (MNAR) can pose serious estimations issues in the linear regression. In this study we evaluate the performance of OLS confidence interval estimates with MNAR data. We also suggest bootstrapping as a remedy for such data cases and compare the traditional confidence intervals against bootstrap ones. As we need to know the true parameters, we carry out a simulations study. Research results indicate that both approaches show similar results having similar intervals size. Given that bootstrap required a lot of computations, traditional methods is still recommended to be used even in case of MNAR

Maqola ma’lumotlari
MualliflarRakhimov, Zarrukh, Rahimova, Nilufar, Рахимов, Заррух, Рахимова, Нилуфар, Рахимов, Заррух, Рахимова, Нилуфар
JurnalИқтисодий тараққиёт ва таҳлил
Nashr sanasi2024-04-30
Jild2
Son4
Betlar492-502
TilIngliz
DOI10.60078/2992-877x-2024-vol2-iss4-pp492-502

Kalit so‘zlar

линейная модель, размер выборки, доверительный интервал, бутстрап, точность, размер интервала, отсутствие не случайно, linear model, sample size, confidence Interval, bootstrap, accuracy, interval size, missing not at random, чизиқли модел, намуна ўлчами, ишонч интервал, юклаш чизиғи, аниқлик, интервал ўлчами, тасодифий эмас

Ilmiy soha

Иқтисодий тараққиёт ва таҳлил jurnalidan boshqa maqolalar

Иқтисодий тараққиёт ва таҳлил — barcha maqolalar