• 품질경영학회지
• /
• 제25권3호
• /
• pp.1-10
• /
• 1997

In the context of accelerated life tests, procedures are given for estimating the parameters in the modified Arrhenius model and for estimating mean life at a given future stress level. The conditional confidence intervals are obtained by conditioning on ancillary statistics and pivotal quantity. Using the data of Tobias and Trindada(1986), we illustrate conditional confidence interval for parameters under use condition in the modified Arrhenius model.

• Journal of the Korean Data and Information Science Society
• /
• 제24권1호
• /
• pp.189-200
• /
• 2013

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation whether the training samples include missing values or not. We consider the conditional bootstrap confidence intervals for classification error rate when a block of observation is missing.

• Communications for Statistical Applications and Methods
• /
• 제13권3호
• /
• pp.503-512
• /
• 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.

• Communications for Statistical Applications and Methods
• /
• 제10권2호
• /
• pp.479-496
• /
• 2003

We investigate logit confidence intervals for the odds ratio based on the delta method. These intervals are constructed using pseudo-Bayes estimators. The Gart method and Agresti method smooth the observed counts toward the model of equiprobability and independence, respectively. We obtain better coverage probability by smoothing the observed counts toward the pseudo-Bayes estimators in 2$\times$2 table. We also improve legit confidence intervals in 2$\times$2$\times$K tables by generalizing these ideas. Utilizing pseudo-Bayes estimators, we obtain better coverage probability by smoothing the observed counts toward the conditional independence model, no three-factor interaction model and saturated model in 2$\times$2$\times$K tables.

• Journal of the Korean Data and Information Science Society
• /
• 제7권1호
• /
• pp.21-35
• /
• 1996

In this paper, estimation and prediction procedures are discussed for grneral situation in which the failure time follows the independent density $f_{i}({\varepsilon}_{i})$ for the accelerated life testing under Type II censoring. In the context of accelerated life test experiment, procedures are given for estimating the parameters in the Eyring model, and for estimating mean life at a given future stress level. The procedures given are conditional confidence interval procedures, obtained by conditioning on ancillary statistics. A comparison is made of these procedures and procedures based on asymptotic properties of the maximum, likelihood estimates.

• Communications for Statistical Applications and Methods
• /
• 제5권3호
• /
• pp.905-926
• /
• 1998

We propose a modified mid P-value method to reduce the conservativeness for the inference of conditional associations in three-way contingency tables. This improves the ordinary mfd P-value method. For $2{\times} 2${\times} K$ tables, we propose confidence intervals for an assumed common odds ratio based on inverting two separate one-sided tests using the modified mid P-value. Also, an alternative and usually even better ways of constructing intervals, based on Inverting a two-sided test, are presented. The actual probability of coverage of a 100($1-\alpha$)% confidence interval is centered about the nominal level, but the modified mid P-value approach gives actual coverage probability even closer to the nominal level than the ordinary mid P-value approach.

• Industrial Engineering and Management Systems
• /
• 제15권2호
• /
• pp.148-155
• /
• 2016

Existing power plants may consume significant amounts of fuel and require high operating costs, partly because of poor electrical power output estimates. This paper suggests a continuous conditional random field (C-CRF) model to predict more precisely the full-load electrical power output of a base load operated combined cycle power plant. We introduce three feature functions to model association potential and one feature function to model interaction potential. Together, these functions compose the C-CRF model, and the model is transformed into a multivariate Gaussian distribution with which the operation parameters can be modeled more efficiently. The performance of our model in estimating power output was evaluated by means of a real dataset and our model outperformed existing methods. Moreover, our model can be used to estimate confidence intervals of the predicted output and calculate several probabilities.

• Communications for Statistical Applications and Methods
• /
• 제28권6호
• /
• pp.627-641
• /
• 2021

An asymmetric least squares estimation method has been employed to estimate linear models for percentile regression. An asymmetric maximum likelihood estimation (AMLE) has been developed for the estimation of Poisson percentile linear models. In this study, we propose generalized nonlinear percentile regression using the AMLE, and the use of the parametric bootstrap method to obtain confidence intervals for the estimates of parameters of interest and smoothing functions of estimates. We consider three conditional distributions of response variables given covariates such as normal, exponential, and Poisson for three mean functions with one linear and two nonlinear models in the simulation studies. The proposed method provides reasonable estimates and confidence interval estimates of parameters, and comparable Monte Carlo asymptotic performance along with the sample size and quantiles. We illustrate applications of the proposed method using real-life data from chemical and radiation epidemiological studies.

• Communications for Statistical Applications and Methods
• /
• 제30권6호
• /
• pp.631-652
• /
• 2023

In the two-condition within-subject mediation design, pairs of variables such as mediator and outcome are observed under two treatment conditions. The main objective of the design is to investigate the indirect effects of the condition difference (sum) on the outcome difference (sum) through the mediator difference (sum) for comparison of two treatment conditions. The natural condition variables mean the original variables, while the rotated condition variables mean the difference and the sum of two natural variables. The outcome difference (sum) is expressed as a linear model regressed on two natural (rotated) mediators as a parallel two-mediator design in two condition approaches: the natural condition approach uses regressors as the natural condition variables, while the rotated condition approach uses regressors as the rotated condition variables. In each condition approach, the total indirect effect on the outcome difference (sum) can be expressed as the sum of two individual indirect effects: within- and cross-condition indirect effects. The total indirect effects on the outcome difference (sum) for both condition approaches are the same. The invariance of the total indirect effect makes it possible to analyze the nature of two pairs of individual indirect effects induced from the natural conditions and the rotated conditions. The two-condition within-subject design is extended to the addition of a between-subject moderator. Probing of the conditional indirect effects given the moderator values is implemented by plotting the bootstrap confidence intervals of indirect effects against the moderator values. The expected indirect effect with respect to the moderator is derived to provide the overall effect of moderator on the indirect effect. The model coefficients are estimated by the structural equation modeling approach and their statistical significance is tested using the bias-corrected bootstrap confidence intervals. All procedures are evaluated using function lavaan() of package {lavaan} in R.

• Asian Pacific Journal of Cancer Prevention
• /
• 제13권11호
• /
• pp.5829-5834
• /
• 2012

Background: Breast cancer is the most common cancer in women worldwide. Although different metabolic factors have been implicated in breast cancer development, the relationship between hypertension and breast cancer has not been elucidated. Aim: To evaluate hypertension as a risk factor for breast cancer in Chilean women of low and middle socio-economic status. Methods: We conducted an age-matched (1:1) case-control study in 3 hospitals in Santiago, Chile. Breast cancer cases (n=170) were histopathologically confirmed. Controls had been classified as Breast Imaging Reporting and Data System I (negative) or II (benign findings) within 6 months of recruitment. Blood pressure was measured using a mercury sphygmomanometer and standardized procedures. We used 2 hypertension cut-off points: blood pressures of ${\geq}140/90$ mmHg and ${\geq}130/85$ mmHg. Fasting insulin and glucose levels were assessed, and anthropometric, sociodemographic, and behavioral information were collected. Odds ratios and 95% confidence intervals were estimated for the entire sample and restricted to postmenopausal women using multivariable conditional logistic regression models. Results: Hypertension (${\geq}140/90$ mmHg) was significantly higher in cases (37.1%) than controls (17.1%) for the entire sample and in postmenopausal pairs (44.0% compared to 23.8%). In crude and adjusted models, hypertensive women had a 4-fold increased risk of breast cancer (adjusted odds ratio: 4.2; 95% confidence interval: 1.8; 9.6) compared to non-hypertensive women in the entire sample. We found a similar association in the postmenopausal group (adjusted odds ratio: 2.8; 95% confidence interval: 1.1; 7.4). A significant effect was also observed when hypertension was defined as blood pressure of ${\geq}130/85$ mmHg. Conclusion: A significant association was found between hypertension and breast cancer over the entire sample and when restricted to postmenopausal women. Hypertension is highly prevalent in Latin America and may be a modifiable risk factor for breast cancer; therefore, a small association between hypertension and breast cancer may have broad implications.