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Showing 7 results for Confidence Interval
J Chachi,
Volume 14, Issue 2 (3-2010)
Abstract
Alireza Shirvani, Dr Mina Towhidi,
Volume 18, Issue 2 (3-2014)
Abstract
So far many confidence intervals were introduced for the binomial proportion. In this paper, our purpose is comparing five well known based on their exact confidence coefficient and average coverage probability.
Mehran Naghizadeh Qomi, Azita Norozi Firoz,
Volume 21, Issue 1 (9-2016)
Abstract
Tolerance interval is a random interval that contains a proportion of the population with a determined confidence level and is applied in many application fields such as reliability and quality control. In this educational paper, we investigate different methods for computing tolerance interval for the binomial random variable using the package Tolerance in statistical software R.
Hossein Nadeb, Hamzeh Torabi,
Volume 21, Issue 1 (9-2016)
Abstract
Censored samples are discussed in experiments of life-testing; i.e. whenever the experimenter does not observe the failure times of all units placed on a life test. In recent years, inference based on censored sampling is considered, so that about the parameters of various distributions such as normal, exponential, gamma, Rayleigh, Weibull, log normal, inverse Gaussian, logistic, Laplace, and Pareto, has been inferred based on censored sampling.
In this paper, a procedure for exact hypothesis testing and obtaining confidence interval for mean of the exponential distribution under Type-I progressive hybrid censoring is proposed. Then, performance of the proposed confidence interval is evaluated using simulation. Finally, the proposed procedures are performed on a data set.
Mr Alireza Shirvani,
Volume 21, Issue 1 (9-2016)
Abstract
A Poisson distribution is well used as a standard model for analyzing count data. So the Poisson distribution parameter estimation is widely applied in practice. Providing accurate confidence intervals for the discrete distribution parameters is very difficult. So far, many asymptotic confidence intervals for the mean of Poisson distribution is provided. It is known that the coverage probability of the confidence interval (L(X),U(X)) is a function of distribution parameter. Since Poisson distribution is discrete, coverage probability of confidence intervals for Poisson mean has no closed form and the exact calculation of confidence coefficient, average coverage probability and maximum coverage probabilities for this intervals, is very difficult. Methodologies for computing the exact average coverage probabilities as well as the exact confidence coefficients of confidence intervals for one-parameter discrete distributions with increasing bounds are proposed by Wang (2009). In this paper, we consider a situation that the both lower and upper bounds of the confidence interval is increasing. In such situations, we explore the problem of finding an exact maximum coverage probabilities for confidence intervals of Poisson mean. Decision about confidence intervals optimality, based on simultaneous evaluation of confidence coefficient, average coverage probability and maximum coverage probabilities, will be more reliable.
, ,
Volume 23, Issue 2 (3-2019)
Abstract
In this paper, in order to establish a confidence interval (general and shortest) for quantiles of normal distribution in the case of one population, we present a pivotal quantity that has non-central t distribution. In the case of two independent normal populations, we construct a confidence interval for the difference quantiles based on the generalized pivotal quantity and introduce a simple method for extracting its percentiles, by which a shorter confidence interval can be constructed. We will also examine the performance of the proposed methods by using simulations and examples.
Dr Nabaz Esmailzadeh, Dr Khosrow Fazli,
Volume 27, Issue 1 (3-2023)
Abstract
In this article, based on a random sample from a normal distribution with unknown parameters, we obtain the shortest confidence interval for the standard deviation parameter using the sample standard deviation. We show that this confidence interval cannot be obtained by taking the square root of the endpoints of the shortest confidence interval for the variance given by Tate and Klett. A table is provided to calculate the confidence interval for several sample sizes and three common confidence coefficients. Also, the power performance of the tests made based on the mentioned confidence intervals is considered.
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