نتایج جستجو برای: process capability index bootstrap confidence interval maximum likelihood estimate type
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A standard model of (conditional) heteroscedasticity, i.e., the phenomenon that the variance of a process changes over time, is the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, which is especially important for economics and finance. GARCH models are typically estimated by the Quasi-Maximum Likelihood (QML) method, which works under mild statistical assumptions. Here...
Background and Objectives: When computing a confidence interval for a binomial proportion p, one must choose an exact interval that has a coverage probability of at least 1-α for all values of p. In this study, we compared the confidence intervals of Clopper-Pearson, Wald, Wilson, and double ArcSin transformation in terms of maintaining a constant nominal type I error. Methods: Simulations w...
A measurement system fit for use is an essential resource to perform quality control activities: its assessment must be carried out periodically quantify bias(location) and precision(width) error qualify it the purpose used. In particular, capability determined evaluate how much of observed variability originates from gauge's precision error. Gauge Repeatability Reproducibility ( R & $R{\&}R$ )...
One of the main goals of studying the time series is estimation of prediction interval based on an observed sample path of the process. In recent years, different semiparametric bootstrap methods have been proposed to find the prediction intervals without any assumption of error distribution. In semiparametric bootstrap methods, a linear process is approximated by an autoregressive process. The...
We derive a profile-likelihood confidence interval and a score based confidence interval to estimate the population prevalences, test sensitivities, and test specificities of two conditionally independent diagnostic tests when no gold standard is available. We are motivated by a real-data example on the study of the properties for two fallible diagnostic tests for bovine immunodeficiency virus....
This paper establishes the higher-order equivalence of the k-step bootstrap, introduced recently by Davidson and MacKinnon (1999), and the standard bootstrap. The k-step bootstrap is a very attractive alternative computationally to the standard bootstrap for statistics based on nonlinear extremum estimators, such as generalized method of moment and maximum likelihood estimators. The paper also ...
1 This paper establishes the higher-order equivalence of the k-step bootstrap, introduced recently by Davidson and MacKinnon (1999), and the standard bootstrap. The k-step bootstrap is a very attractive alternative computationally to the standard bootstrap for statistics based on nonlinear extremum estimators, such as generalized method of moment and maximum likelihood estimators. The paper als...
A total of 63 families with Huntington disease (HD) were examined for linkage between HD and G8 (D4S10). The families included 57 Caucasian, four Black American, and two Japanese. The combined maximum lod score was 87.69 at theta = 0.04 (99% confidence interval 0.018-0.071). The maximum frequency of recombination was 0.03 in males and 0.05 in females. Fifty-seven families gave positive lod scor...
Synopsis Mixed stock analysis (MSA) is used to estimate the relative contributions of distinct populations in a mixture of organisms, generally via conditional maximum likelihood estimation using a baseline of learning samples from all potentially contributing populations. MSA is increasingly used to judge the presence or absence of specific populations in specific mixture samples. This is comm...
We derive three likelihood-based confidence intervals for the risk ratio of two proportion parameters using a double sampling scheme for misclassified binomial data. The risk ratio is also known as the relative risk. We obtain closed-form maximum likelihood estimators of the model parameters by maximizing the full-likelihood function. Moreover, we develop three confidence intervals: a naive Wal...
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