• Title/Summary/Keyword: Bootstrap confidence interval

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A Comparison Study for the Confidence Intervals of the Common Odds Ratio in the Stratified 2 X 2 Tables Using the Average Coverage Probability

  • Kwak, Min Jung;Jeong, Hyeong Chul
    • Communications for Statistical Applications and Methods
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    • v.10 no.3
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    • pp.779-793
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    • 2003
  • In this paper, various methods for finding confidence intervals for common odds ratio $\psi$ of the K 2${\times}$2 tables are reviewed. Also we propose two jackknife confidence intervals and bootstrap confidence intervals for $\psi$. These confidence intervals are compared with the other existing confidence intervals by using Monte Carlo simulation with respect to the average coverage probability.

Bootstrapping Logit Model

  • Kim, Dae-hak;Jeong, Hyeong-Chul
    • Communications for Statistical Applications and Methods
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    • v.9 no.1
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    • pp.281-289
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    • 2002
  • In this paper, we considered an application of the bootstrap method for logit model. Estimation of type I error probability, the bootstrap p-values and bootstrap confidence intervals of parameter were proposed. Small sample Monte Carlo simulation were conducted in order to compare proposed method with existing normal theory based asymptotic method.

Bootstrap Confidence Intervals for the Difference of Quantiles of Right Censored Data

  • Na, Jong-Hwa;Park, Hyo-Il;Jang, Young-Mi
    • Communications for Statistical Applications and Methods
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    • v.11 no.3
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    • pp.447-454
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    • 2004
  • In this paper, we consider the bootstrap method to the interval estimation of the difference of quantiles of right censored data. We showed the validity of bootstrap method and compare with others with real data example. In simulation various resampling schemes for right censored data are also considered.

On Employing Nonparametric Bootstrap Technique in Oscillometric Blood Pressure Measurement for Confidence Interval Estimation

  • Lee, Yong-Kook;Lee, Im-Bong;Chang, Joon-Hyuk;Lee, Soo-Jeong
    • Journal of Korea Multimedia Society
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    • v.17 no.2
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    • pp.200-207
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    • 2014
  • Blood pressure (BP) is an important vital signal for determining the health of an individual subject. Although estimation of mean arterial blood pressure is possible using oscillometric blood pressure techniques, there are no established techniques in the literature for obtaining confidence interval (CI) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates obtained from such BP measurements. This paper proposes a nonparametric bootstrap technique to obtain CI with a small number of the BP measurements. The proposed algorithm uses pseudo measurements employing nonparametric bootstrap technique to derive the pseudo maximum amplitudes (PMA) and the pseudo envelopes (PE). The SBP and DBP are then derived using the new relationships between PMA and PE and the CIs for such estimates. Application of the proposed method on an experimental dataset of 85 patients with five sets of measurements for each patient has yielded a smaller Cl than the conventional student t-method.

A Comparison of Confidence Intervals for the Difference of Proportions (모비율 차이의 신뢰구간들에 대한 비교연구)

  • 정형철;전명식;김대학
    • The Korean Journal of Applied Statistics
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    • v.16 no.2
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    • pp.377-393
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    • 2003
  • Several confidence interval estimates for the difference of two binomial proportions were introduced. Bootstrap confidence interval is also suggested. We examined the over estimation property of approximate intervals and under estimation trend of exact intervals for the difference of proportions. We compared these confidence intervals based on the average coverage probability, expected width and skewness measure. Particularly actual coverage probability were calculated by using the prior distribution of parameters. Monte Carlo simulation for small sample size is conducted. Some interesting contour plots of average coverage probability and marginal plots for several interval estimates are presented.

Bootstrap Confidence Interval of Treatment Effect for Censored Data

  • Hyun Jong KIM;Sang Gue PARK
    • Communications for Statistical Applications and Methods
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    • v.4 no.3
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    • pp.917-927
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    • 1997
  • Consider the confidence interval estimators of treatment effect when some of data to be analyzed are randomly censored, assuming two-sample location-shift model. Recently proposed PARK and PARK(1995) Estimators is discussed and a bootstrap estimator is proposed. This estimator is compared with other well-known estimators throught the simulation studies and recommendations about the use are made.

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Bootstrap Confidence Intervals for an Adjusted Survivor Function under the Dependent Censoring Model

  • Lee, Seung-Yeoun;Sok, Yong-U
    • Communications for Statistical Applications and Methods
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    • v.8 no.1
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    • pp.127-135
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    • 2001
  • In this paper, we consider a simple method for testing the assumption of independent censoring on the basis of a Cox proportional hazards regression model with a time-dependent covariate. This method involves a two-stage sampling in which a random subset of censored observations is selected and followed-up until their true survival times are observed. Lee and Wolfe(1998) proposed an adjusted estimate of the survivor function for the dependent censoring under a proportional hazards alternative. This paper extends their result to obtain a bootstrap confidence interval for the adjusted survivor function under the dependent censoring. The proposed procedure is illustrated with an example of a clinical trial for lung cancer analysed in Lee and Wolfe(1998).

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On Statistical Inference of Stratified Population Mean with Bootstrap (층화모집단 평균에 대한 붓스트랩 추론)

  • Heo, Tae-Young;Lee, Doo-Ri;Cho, Joong-Jae
    • Communications for Statistical Applications and Methods
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    • v.19 no.3
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    • pp.405-414
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    • 2012
  • In a stratified sample, the sampling frame is divided into non-overlapping groups or strata (e.g. geographical areas, age-groups, and genders). A sample is taken from each stratum, if this sample is a simple random sample it is referred to as stratified random sampling. In this paper, we study the bootstrap inference (including confidence interval) and test for a stratified population mean. We also introduce the bootstrap consistency based on limiting distribution related to the plug-in estimator of the population mean. We suggest three bootstrap confidence intervals such as standard bootstrap method, percentile bootstrap method and studentized bootstrap method. We also suggest a bootstrap test method computing the $ASL_{boot}$(Achieved Significance Level). The results of estimation are verified using simulation.

Empirical Bayes Interval Estimation by a Sample Reuse Method

  • Cho, Kil-Ho;Choi, Dal-Woo;Chae, Hyeon-Sook
    • Journal of the Korean Data and Information Science Society
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    • v.8 no.1
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    • pp.41-48
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    • 1997
  • We construct the empirical Bayes(EB) confidence intervals that attain a specified level of EB coverage for the unknown scale parameter in the Weibull distribution with the known shape parameter under the type II censored data. Our general approach is to use an EB bootstrap samples introduced by Larid and Louis(1987). Also, we compare the coverage probability and the expected interval length for these bootstrap intervals with those of the naive intervals through Monte Carlo simulation.

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Bootstrapping Unified Process Capability Index

  • Cho, Joong-Jae;Han, Jeong-Hye;Jo, See-Heyon
    • Journal of the Korean Statistical Society
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    • v.26 no.4
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    • pp.543-554
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    • 1997
  • A family of some capability indices { $C_{p}$(.alpha.,.beta.); .alpha..geq.0, .beta..geq.0}, containing the indices $C_{p}$, $C_{{pk}}$, $C_{{pm}}$, and $C_{{pmk}}$, has been defined by Vannman(1993) for the case of two-sided specification interval. By varying the parameters of the family various capability indices with suitable properties are obtained. We derive tha asymptotic distribution of the family { $C_{p}$(.alpha.,.beta.); .alpha..geq.0,.beta..geq.0} under general proper conditions. It is also shown that the bootstrap approximation to the distribution of the estimator $C_{p}$(.alpha., .beta.) is vaild for almost all sample sequences. These asymptotic distributions would be used in constructing some bootstrap confidence intervals.tervals.

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