Presentation of a Confidence Interval Estimate as Evidence in a Legal Proceeding

1975 ◽  
Vol 29 (4) ◽  
pp. 138 ◽  
Author(s):  
Leo Katz
Keyword(s):  
Confidence Interval ◽  
Interval Estimate ◽  
Legal Proceeding

Method for Constructing a Confidence Interval for the Variance of a Random Variable

Vestnik NSUEM ◽  
2021 ◽  
pp. 146-155
Author(s):  
A. V. Ganicheva ◽  
A. V. Ganichev
Keyword(s):  
Probability Distribution ◽  
Confidence Interval ◽  
Random Variable ◽  
New Method ◽  
Interval Estimate ◽  
Normal Law

The problem of reducing the number of observations for constructing a confidence interval of variance with a given degree of accuracy and reliability is considered. The new method of constructing an interval estimate of variance developed in the article is formulated by three statements and justified by four proven theorems. Formulas for calculating the required number of observations depending on the accuracy and reliability of the estimate are derived. The results of the calculations are presented in the table and shown in the diagram. The universality and effectiveness of this method is shown. The universality of the method lies in the fact that it is applicable to any laws of probability distribution, and not only for the normal law. The effectiveness of the developed method is justified by comparing its performance with other known methods.

Estimation of Disease Incidence in Fish Populations

1983 ◽  
Vol 40 (12) ◽  
pp. 2194-2197 ◽  
Author(s):  
Donald D. Worlund ◽  
Gib Taylor
Keyword(s):  
Confidence Interval ◽  
Binomial Distribution ◽  
Pacific Salmon ◽  
Disease Incidence ◽  
Sampling Plan ◽  
Fish Populations ◽  
Interval Estimate ◽  
Large Populations ◽  
Juvenile Pacific Salmon

A method is described to estimate the disease incidence in large populations of fish where the material analyzed is composed of a number of pooled individuals. A procedure for calculating a confidence interval estimate is presented, and the bias of the estimate discussed. The methods were developed for estimating disease incidence in hatchery populations of juvenile Pacific salmon (Oncorhynchus spp.), but are applicable to any situation in which the sampling plan can be assumed to follow the binomial distribution.

Inferential Methods for the Tetrachoric Correlation Coefficient

2005 ◽  
Vol 30 (2) ◽  
pp. 213-225 ◽  
Author(s):  
Douglas G. Bonett ◽  
Robert M. Price
Keyword(s):  
Confidence Interval ◽  
Sample Size ◽  
Standard Error ◽  
Point Estimate ◽  
Interval Estimate ◽  
Tetrachoric Correlation ◽  
Continuous Variables ◽  
Simple Method ◽  
Estimate Standard Error ◽  
Better Than

The tetrachoric correlation describes the linear relation between two continuous variables that have each been measured on a dichotomous scale. The treatment of the point estimate, standard error, interval estimate, and sample size requirement for the tetrachoric correlation is cursory and incomplete in modern psychometric and behavioral statistics texts. A new and simple method of accurately approximating the tetrachoric correlation is introduced. The tetrachoric approximation is then used to derive a simple standard error, confidence interval, and sample size planning formula. The new confidence interval is shown to perform far better than the confidence interval computed by SAS. A method to improve the SAS confidence interval is proposed. All of the new results are computationally simple and are ideally suited for textbook and classroom presentations.

scholarly journals Data Transformation for Confidence Interval Improvement: An Application to the Estimation of Stress-Strength Model Reliability

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Alessandro Barbiero
Keyword(s):  
Confidence Interval ◽  
Proper Function ◽  
Special Focus ◽  
Interval Estimate ◽  
Reliability Parameter ◽  
True Value ◽  
The Central Limit Theorem ◽  
Model Reliability ◽  
Strength Models ◽  
Improve Standard

In many statistical applications, it is often necessary to obtain an interval estimate for an unknown proportion or probability or, more generally, for a parameter whose natural space is the unit interval. The customary approximate two-sided confidence interval for such a parameter, based on some version of the central limit theorem, is known to be unsatisfactory when its true value is close to zero or one or when the sample size is small. A possible way to tackle this issue is the transformation of the data through a proper function that is able to make the approximation to the normal distribution less coarse. In this paper, we study the application of several of these transformations to the context of the estimation of the reliability parameter for stress-strength models, with a special focus on Poisson distribution. From this work, some practical hints emerge on which transformation may more efficiently improve standard confidence intervals in which scenarios.

The Nonparametric Confidence Interval for the Process Capability Index C*pmk

2013 ◽  
Vol 404 ◽  
pp. 520-525 ◽  
Author(s):  
Shu Fei Wu
Keyword(s):  
Confidence Interval ◽  
Coverage Probability ◽  
Process Capability ◽  
Process Capability Index ◽  
The Other ◽  
Interval Estimate ◽  
Jackknife Method ◽  
Capability Index ◽  
Simulation Results ◽  
Bootstrap Interval

The process capability index which is a generalization of is defined by the use of the idea of Chan et al. [ for asymmetric tolerance. In this paper, we proposed a Jackknife confidence interval for and compare its coverage probability with the other three Efron and Tibshiranis [ bootstrap interval estimate techniques. The simulation results show that the Jackknife method has higher chance of reaching the nominal confidence coefficient for all cases considered in this paper. Therefore this method is recommended for used. One numerical example to demonstrate the construction of confidence interval for the process capability index is also given in this paper.

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