scholarly journals Robust Confidence Intervals for the Population Mean Alternatives to the Student-t Confidence Interval

2020 ◽  
Vol 18 (1) ◽  
pp. 2-20
Author(s):  
Moustafa Omar Ahmed Abu-Shawiesh ◽  
Aamid Saghir
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Simulation Study ◽  
Real Life ◽  
Population Mean ◽  
Life Data ◽  
Real Life Data ◽  
Student’S T

In this paper, three robust confidence intervals are proposed as alternatives to the Student t confidence interval. The performance of these intervals was compared through a simulation study shows that Qn-t confidence interval performs the best and it is as good as Student’s t confidence interval. Real-life data was used for illustration and performing a comparison that support the findings obtained from the simulation study.

scholarly journals Some Exponential Type Predictive Estimators of Finite Population Mean in Two-Phase Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 51-57
Author(s):  
Asifa Kamal ◽  
Nimra Amir ◽  
Huma Dastagir
Keyword(s):  
Simulation Study ◽  
Finite Population ◽  
Numerical Study ◽  
Real Life ◽  
Data Sets ◽  
Two Phase ◽  
Population Mean ◽  
Real Life Data ◽  
Phase Sampling ◽  
Two Phase Sampling

This study is designed for predictive estimation of finite population mean in two phase sampling using two auxiliary variables. Two phase exponential ratio type estimator and exponential chain ratio type estimator are proposed under predictive approach suggested by Bahl & Tuteja (1991). The expressions of bias and mean square error of both suggested estimators have been carried out for theoretical comparison. The numerical study on real life data sets as well as simulation study has been conducted to examine the performance of suggested estimators. Finally, it is demonstrated that the suggested exponential ratio estimators are more efficient than competitive estimators in support of numerical and simulation study as well.

scholarly journals Asymptotic curved normal distribution

2019 ◽  
Vol 52 (2) ◽  
pp. 173-186
Author(s):  
C. SATHEESH KUMAR ◽  
G. V. ANILA
Keyword(s):  
Maximum Likelihood ◽  
Normal Distribution ◽  
Simulation Study ◽  
Real Life ◽  
Skew Normal Distribution ◽  
Data Set ◽  
New Class ◽  
Life Data ◽  
Real Life Data ◽  
Method Of Maximum Likelihood

Here we introduce a new class of skew normal distribution as a generalization of the extended skew curved normal distribution of Kumar and Anusree (J. Statist. Res., 2017) and investigate some of its important statistical properties. The location-scale extension of the proposed class of distribution is also defined and discussed the estimation of its parameters by method of maximum likelihood. Further, a real life data set is considered for illustrating the usefulness of the model and a brief simulation study is attempted for assessing the performance of the estimators.

scholarly journals An alternative Laplace distribution

2020 ◽  
Vol 53 (2) ◽  
pp. 111-127
Author(s):  
C. Satheesh Kumar ◽  
Rosmi Jose
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Simulation Study ◽  
Real Life ◽  
Likelihood Estimation ◽  
Laplace Distribution ◽  
Data Sets ◽  
Life Data ◽  
Real Life Data ◽  
Alternative Version

In this paper, we propose an alternative version to the Laplace distribution which we named as “alternative Laplace distribution (ALD)” and discuss some of its important properties. A location-scale extension of the ALD is considered and the maximum likelihood estimation procedures for estimating its parameters is described. Further, the distribution is fitted to certain real life data sets for illustrating the utility of the model. A simulation study is carried out to examine the performance of likelihood estimators of the parameters of the distribution.

A Review of Bootstrap Methods in Ranked Set Sampling

2022 ◽  
pp. 171-189
Author(s):  
Arpita Chatterjee ◽  
Santu Ghosh
Keyword(s):  
Confidence Interval ◽  
Bootstrap Method ◽  
Real Life ◽  
Bootstrap Confidence Interval ◽  
Percentile Method ◽  
Life Data ◽  
Real Life Data ◽  
Bootstrap Percentile ◽  
The Mean ◽  
Method Bias

This chapter provides a brief review of the existing resampling methods for RSS and its implementation to construct a bootstrap confidence interval for the mean parameter. The authors present a brief comparison of these existing methods in terms of their flexibility and consistency. To construct the bootstrap confidence interval, three methods are adopted, namely, bootstrap percentile method, bias-corrected and accelerated method, and method based on monotone transformation along with normal approximation. Usually, for the second method, the accelerated constant is computed by employing the jackknife method. The authors discuss an analytical expression for the accelerated constant, which results in reducing the computational burden of this bias-corrected and accelerated bootstrap method. The usefulness of the proposed methods is further illustrated by analyzing real-life data on shrubs.

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

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