• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.1047-1056
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• 2003

In this paper, we consider a modified bootstrap scheme for inference of the GPH estimator and establish the sup-norm consistency of the proposed bootstrapping.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.503-512
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• 2006

In this paper, the Inverse Weibull distribution parameters have been estimated using a new estimation technique based on the non-parametric kernel density function that introduced as an alternative and reliable technique for estimation in life testing models. This technique will require bootstrapping from a set of sample observations for constructing the density functions of pivotal quantities and thus the confidence intervals for the distribution parameters. The performances of this technique have been studied comparing to the conditional inference on the basis of the mean lengths and the covering percentage of the confidence intervals, via Monte Carlo simulations. The simulation results indicated the robustness of the proposed method that yield reasonably accurate inferences even with fewer bootstrap replications and it is easy to be used than the conditional approach. Finally, a numerical example is given to illustrate the densities and the inferential methods developed in this paper.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.341-350
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• 2012

Composite quantile regression model is considered for iid error case. Since the regression coefficients are the same across different quantiles, composite quantile regression can be used to combine the strength across multiple quantile regression models. For the composite quantile regression, bootstrap method is examined for statistical inference including the selection of the number of quantiles and confidence intervals for the regression coefficients. Feasibility of the bootstrap method is demonstrated through a simulation study.

• Animal cells and systems
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• v.4 no.1
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• pp.31-37
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• 2000

The genetic variations among six species of Rana from Korea (R. nigro-maculata, R. piancyi, R. dybowskii, R. sp, R. rugosa type A, B and R. amurensis) were investigated using 499 bases of mitochondrial DNA sequences for ND2 (NADH dehydrogenase subunit 2) gene and $tRNA^{Trp}$ gene. Partial sequences of ND2 gene (427 bp) and full sequences of $tRNA^{Trp}$ gene (73 bp) were identified. The level of sequence divergences ranged from 0.2 to 5.2% within species and 4.9-28.0% among 6 species of the genus Rana. The $tRNA^{Trp}$ gene of the genus Rana was composed of 77 nucleotides which showed a two dimensional "cloverleaf" structure. The secondary structure of $tRNA^{Trp}$ was not found compensatory changes which could potentially confound phylogenetic inference. In the neighborjoining tree, brown frogs were clustered first with the level of sequence divergence of 13.20% between R. amurensis and R. dybowskii, and 9% between R. dybowskii and R. sp. supported by 99% bootstrap iterations, respectively. R. nigromaculata and R. plancyi were clustered into another group with 5.1% divergence supported by 100% bootstrap iteration. R. rugosa A 8nd B types were grouped by 4.9% divergence and clustered into the last group with other two groups with 100% bootstrap iterations.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.269-285
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• 2016

In this paper, a simple step-stress accelerated life test (ALT) under progressive type-II censoring is considered. Progressive type-II censoring and accelerated life testing are provided to decrease the lifetime of testing and lower test expenses. The cumulative exposure model is assumed when the lifetime of test units follows an extension of the exponential distribution. Maximum likelihood estimates (MLEs) and Bayes estimates (BEs) of the model parameters are also obtained. In addition, a real dataset is analyzed to illustrate the proposed procedures. Approximate, bootstrap and credible confidence intervals (CIs) of the estimators are then derived. Finally, the accuracy of the MLEs and BEs for the model parameters is investigated through simulation studies.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.227-237
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• 2004

In this paper we presented a Bayesian inference approach for estimating the location and scale parameters of the lognormal distribution using iterative Gibbs sampling algorithm. We also presented estimation of location parameter by two non iterative methods, importance sampling and weighted bootstrap assuming scale parameter as known. The estimates by non iterative techniques do not depend on the specification of hyper parameters which is optimal from the Bayesian point of view. The estimates obtained by more sophisticated Gibbs sampler vary slightly with the choices of hyper parameters. The objective of this paper is to illustrate these tools in a simpler setup which may be essential in more complicated situations.

• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.175-185
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• 1996

For the Change-point problem in a sequence of binomial variables we consider the maximum likelihood estimator (MLE) of unknown change-point. Its asymptotic distribution is quite limited in the case of binomial variables with different numver of trials at each time point. Hinkley and Hinkley (1970) gives an asymptotic distribution of the MLE for a sequence of Bernoulli random variables. To find the asymptotic distribution a numerical method such as bootstrap can be used. Another concern of our interest in the inference on the change-point and we derive confidence sets based on the liklihood ratio test(LRT). We find approximate confidence sets from the bootstrap distribution and compare the two results through an example.

• Journal of Life Science
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• v.14 no.4
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• pp.593-607
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• 2004

The nucleotide sequence for a nuclear-encoded small subunit rDNA (SSU rDNA) was determined for 43 species of the class Dinophyceae, including harmful algae Cochlodinium polykrikoides and Gyrodinium aureolum. These sequences and data analyses were performed by parsimony, distances and maximum likelihood methods in PHYLIP (Phylogenetic Inference Package) version 3.573c. The species Noctiluca scintillans, Gonyaulax spinifern and Crypthecodinium cohnii occupied a basal position within the Dino- phyceae in our analyses. The genera Alexandrium and Symbiodinium were monophyletic (supported by a bootstrap value of >70%), whereas the genera Gymnedinium and Gyrodinium formed polyphyletic nodes, for which bootstrap support was strong (>70%) in the neighbor-joining and maximum likelihood methods except for the PHYLIP parsimony analysis (=59%). The sequence divergence between G. aureolum and G. dorsum/ G. galathenum was the largest at 7.4% (45 bp), whereas G. aureolum and G. mikimotoi showed an extremely low value of genetic divergence of 0.9% (5 bp). The genetic divergence between C. polykrikoides and G. aureolum was a low value of 5.2% (31 bp). In the phylogenetic analysis, the placement of G. aureolum and C. polykrikoides was closer to the genus Gymnodinium than to the genus Gyrodinium, which was supported by a moderate bootstrap value.

• Genomics & Informatics
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• v.9 no.4
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• pp.181-188
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• 2011

In planning a model-based phylogenic study for highly related ethnic data, the SNP marker number is an important factor to determine for relationship inferences. Genotype frequency data, utilizing a sub sampling method, from 63 Pan Asian ethnic groups was used for determining the minimum SNP number required to establish such relationships. Bootstrap random sub-samplings were done from 5.6K PASNPi SNP data. DA distance was calculated and neighbour-joining trees were drawn with every re-sampling data set. Consensus trees were made with the same 100 sub-samples and bootstrap proportions were calculated. The tree consistency to the one obtained from the whole marker set, improved with increasing marker numbers. The bootstrap proportions became reliable when more than 7,000 SNPs were used at a time. Within highly related ethnic groups, the minimum SNPs number for a robust neighbor-joining tree inference was about 7,000 for a 95% bootstrap support.

• Journal of the Korean Statistical Society
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• v.19 no.2
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• pp.139-144
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• 1990

For the first-order bilinear time series model $X_t = aX_{t-1} + e_i + be_{t-1}X_{t-1}$ where ${e_i}$ is a sequence of independent normal random variables with mean 0 and variance $\sigma^2$, the asymptotic distribution of sample autocarrelation function is obtained and shown to follow a normal distribution. The variance of the asymptotic distribution is of a complicated form and hence a bootstrap estimate of the variance is proposed for large sample inference. This result can be used to distinguish between different bilinear models.