Results 231 to 240 of about 291,506 (257)

Nonparametric Estimation of a Bivariate Mean Residual Life Function

Journal of the American Statistical Association, 2002
In many statistical studies involving failure data, mean residual life function is of prime importance. The bivariate mean residual life function has received relatively less attention in the literature. In this article we use a simple nonparametric estimator for a bivariate mean residual life function.
Kulkarni H.V., Rattihalli R.N.
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Some New Bounds for Moment Generating Functions of Various Life Distributions Using Mean Residual Life Functions

Journal of Statistical Theory and Practice, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Steven G. From, Suthakaran Ratnasingam
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Tests and Confidence Sets for Comparing Two Mean Residual Life Functions

Biometrics, 1988
The mean residual life function of a population gives an intuitive and interesting perspective on the aging process. Here we present new nonparametric methods for comparing mean residual life functions based on two independent samples. These methods have the flexibility to handle crossings of the functions and result in a new type of confidence set. We
R L, Berger, D D, Boos, F M, Guess
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Asymptotic Decompositions for Numerical Characteristics of the Estimator of a Mean Residual Life Function

Journal of Mathematical Sciences, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Abdushukurov, A. A., Sagidullaev, K. S.
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Nonparametric estimation of the conditional mean residual life function with censored data

Lifetime Data Analysis, 2011
The conditional mean residual life (MRL) function is the expected remaining lifetime of a system given survival past a particular time point and the values of a set of predictor variables. This function is a valuable tool in reliability and actuarial studies when the right tail of the distribution is of interest, and can be more informative than the ...
McLain, Alexander C., Ghosh, Sujit K.
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Jackknife empirical likelihood test for mean residual life functions

Communications in Statistics - Theory and Methods, 2016
ABSTRACTMean residual life (MRL) function is an important function in survival analysis which describes the expected remaining life given survival to a certain age. In this article, we propose a non parametric method based on jackknife empirical likelihood through a U-statistic to test the equality of two mean residual functions.
Ying-Ju Chen, Wei Ning, Arjun K. Gupta
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Mean residual life function for normal, gamma and lognormal densities

Reliability Engineering, 1983
The evaluation of reliability function for normal, gamma and lognormal failure densities involves numerical integration. In order to evaluate mean residual life function one has to integrate the reliability function which involves numerical integration once again.
K.K. Govil, K.K. Aggarwal
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Asymptotic approximations to the normal hazard function and mean residual life function

1990
This note proposes asymptotic approximations to the hazard function and the mean residual life function, and their inverse, of the normal distribution. They are very easily recalled expressions and are convenient for use on a pocket calculator.
Calabria R, Pulcini G
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Inference for the Mean Residual Life Function via Empirical Likelihood

Communications in Statistics - Theory and Methods, 2006
In addition to the distribution function, the mean residual life (MRL) function is the other important function which can be used to characterize a lifetime in survival analysis and reliability. For inference on the MRL function, some procedures have been proposed in the literature.
Yichuan Zhao, Gengsheng Qin
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MEAN RESIDUAL LIFE FUNCTION FOR ADDITIVE AND MULTIPLICATIVE HAZARD RATE MODELS

Probability in the Engineering and Informational Sciences, 2015
This paper deals with the mean residual life function (MRLF) and its monotonicity in the case of additive and multiplicative hazard rate models. It is shown that additive (multiplicative) hazard rate does not imply reduced (proportional) MRLF and vice versa. Necessary and sufficient conditions are obtained for the two models to hold simultaneously.
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