Estimation of a Biometric Function

Yang, Grace L.

doi:10.1214/aos/1176344070

Cited by 112 publications

(48 citation statements)

References 4 publications

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“…Using the simulated cut-off points given in Table 1 we report in the next table the empirical power estimates when the true underlying distribution is (i) the log-normal distribution with mean zero and standard deviation 0.04; i.e. LN (0, 0.4) and (ii) the distribution defined in (12).…”

Section: Power Of the Testsmentioning

confidence: 99%

“…Characterization results and other important properties of MRL functions are discussed in Bhattacharjee [10] and Kirmani and Gupta [11]. Estimation of the MRL function was first considered by Yang [12] by replacing the survival function with its empirical counterpart. Chaubey and Sen [13] and Chaubey and Sen [14] proposed an alternative smoothed estimator based on complete data and right-censored data respectively.…”

Section: Introductionmentioning

confidence: 99%

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Monte Carlo comparison of tests of exponentiality against NWBUE alternatives

Anis

Ghosh

2015

Mathematics and Computers in Simulation

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Section: Power Of the Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monte Carlo comparison of tests of exponentiality against NWBUE alternatives

Anis

Ghosh

2015

Mathematics and Computers in Simulation

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“…Mean residual life function is a conditional concept to describe the average remaining lifetime given the fact that the subject survived at least t. Yang [20] constructed an empirical estimator for mean residual life function based on order statistics, and cleared the estimator's uniform asymptotic behavior. Later, Hall and Wellner [5] and Csörgő and Zitikis [2] further developed Yang's estimator.…”

Section: Introductionmentioning

confidence: 99%

Quantile residual lifetime for left-truncated and right-censored data

2014

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This article proposes a simple nonparametric estimator of quantile residual lifetime function under left-truncated and right-censored data. The asymptotic consistency and normality of this estimator are proved and the variance expression is calculated. Two bootstrap procedures are employed in the simulation study, where the latter bootstrap from Zeng & Lin (2008) is 4000 times faster than the former naive one, and the numerical results in both methods show that our estimating approach works well. A real data example is used to illustrate its application. Keywordsleft-truncated, right-censored, quantile residual lifetime, empirical process MSC(2010) 62N01, 90B25Citation: Wang Y X, Liu P, Zhou Y. Quantile residual lifetime for left-truncated and right-censored data.

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“…Using the closed relationship between M(t 0 ) and the survival function S(t) = 1 − F(t), many nonparametric methods are developed to construct estimators for M(t 0 ). For complete data, consistent estimators of MRL are proposed by Yang (1978).…”

Section: Introductionmentioning

confidence: 99%

Likelihood ratio inference for mean residual life

Shen

Liang

2010

Stat Papers

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One of the basic parameters in survival analysis is the mean residual life M 0 . For right censored observation, the usual empirical likelihood based log-likelihood ratio leads to a scaled χ 2 1 limit distribution and estimating the scaled parameter leads to lower coverage of the corresponding confidence interval. To solve the problem, we present a log-likelihood ratio l(M 0 ) by methods of Murphy and van der Vaart (Ann Stat 1471-1509, 1997). The limit distribution of l(M 0 ) is the standard χ 2 1 distribution. Based on the limit distribution of l(M 0 ), the corresponding confidence interval of M 0 is constructed. Since the proof of the limit distribution does not offer a computational method for the maximization of the log-likelihood ratio, an EM algorithm is proposed. Simulation studies support the theoretical result.

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Estimation of a Biometric Function

Cited by 112 publications

References 4 publications

Monte Carlo comparison of tests of exponentiality against NWBUE alternatives

Monte Carlo comparison of tests of exponentiality against NWBUE alternatives

Quantile residual lifetime for left-truncated and right-censored data

Likelihood ratio inference for mean residual life

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