Wyniki wyszukiwania - Biblioteka Nauki

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: Confidence interval

Sortuj według:

Ogranicz wyniki do:

Empirical likelihood for quantile regression models with response data missing at random

100%

Luo S. , Pang S.

Open Mathematics

2017

tom 15

nr 1

317-330

This paper studies quantile linear regression models with response data missing at random. A quantile empirical-likelihood-based method is proposed firstly to study a quantile linear regression model with response data missing at random. It follows that a class of quantile empirical log-likelihood ratios including quantile empirical likelihood ratio with complete-case data, weighted quantile empirical likelihood ratio and imputed quantile empirical likelihood ratio are defined for the regression parameters. Then, a bias-corrected quantile empirical log-likelihood ratio is constructed for the mean of the response variable for a given quantile level. It is proved that these quantile empirical log-likelihood ratios are asymptotically χ2 distribution. Furthermore, a class of estimators for the regression parameters and the mean of the response variable are constructed, and the asymptotic normality of the proposed estimators is established. Our results can be used directly to construct the confidence intervals (regions) of the regression parameters and the mean of the response variable. Finally, simulation studies are conducted to assess the finite sample performance and a real-world data set is analyzed to illustrate the applications of the proposed method.

Improved Estimators of Coefficient of Variation in a Finite Population

88%

Archana V. , Aruna Rao K.

Statistics in Transition new series

2011

tom 12

nr 2

357-380

Coefficient of Variation (C.V) is a unitless measure of dispersion. Hence it is widely used in many scientific and social investigations. Although a lot of work has been done concerning C.V in the infinite population models, it has been neglected in the finite populations. Many areas of applications of C.V involves the finite populations like the use in official statistics and economic surveys of the World Bank. This has motivated us to propose six new estimators of the population C.V. In finite population studies regression estimators are widely used and the idea is exploited to propose the new estimators. Three of the proposed estimators are the regression estimators of the C.V for the study variable while the other three estimators makes use of the regression estimators of population mean and variance to estimate the ratio , the population C.V for the study variable. The bias and mean square error (MSE) of these estimators were derived for the simple random sampling design. The performance of these estimators is compared using two real life data sets. The simulation is carried out to compare the estimators in terms of coverage probability and the length of the confidence interval. The small sample comparison indicates that two of the proposed estimators perform better than the sample C.V. The regression estimator using the information on the Population C.V of the auxiliary variable emerges as the best estimator.