scholarly journals Statistical Analysis of Location Parameter of Inverse Gaussian Distribution Under Noninformative Priors

2019 ◽  
Vol 3 (2) ◽  
pp. 62-76
Author(s):  
Nida Khan ◽  
Muhammad Aslam
Keyword(s):  
Gaussian Distribution ◽  
Shape Parameter ◽  
Real Life ◽  
Location Parameter ◽  
Inverse Gaussian Distribution ◽  
Inverse Gaussian ◽  
Noninformative Priors ◽  
Life Data ◽  
Squared Error ◽  
Real Life Data

Bayesian estimation for location parameter of the inverse Gaussian distribution is presented in this paper. Noninformative priors (Uniform and Jeffreys) are assumed to be the prior distributions for the location parameter as the shape parameter of the distribution is considered to be known. Four loss functions: Squared error, Trigonometric, Squared logarithmic and Linex are used for estimation. Bayes risks are obtained to find the best Bayes estimator through simulation study and real life data

scholarly journals Calibration-Based Estimators using Different Distance Measures under Two Auxiliary Variables: A Comparative Study

2021 ◽  
Vol 19 (1) ◽  
pp. 2-20
Author(s):  
Piyush Kant Rai ◽  
Alka Singh ◽  
Muhammad Qasim
Keyword(s):  
Mean Squared Error ◽  
Real Life ◽  
Distance Functions ◽  
Distance Measures ◽  
Auxiliary Variables ◽  
Data Set ◽  
Life Data ◽  
Squared Error ◽  
Real Life Data ◽  
Relative Root

This article introduces calibration estimators under different distance measures based on two auxiliary variables in stratified sampling. The theory of the calibration estimator is presented. The calibrated weights based on different distance functions are also derived. A simulation study has been carried out to judge the performance of the proposed estimators based on the minimum relative root mean squared error criterion. A real-life data set is also used to confirm the supremacy of the proposed method.

scholarly journals Estimation for inverse Gaussian distribution under first-failure progressive hybird censored samples

Filomat ◽  
2017 ◽  
Vol 31 (18) ◽  
pp. 5743-5752 ◽  
Author(s):  
Jun-Mei Jia ◽  
Zai-Zai Yan ◽  
Xiu-Yun Peng
Keyword(s):  
Gaussian Distribution ◽  
Information Matrix ◽  
Real Life ◽  
Inverse Gaussian Distribution ◽  
Type I ◽  
Inverse Gaussian ◽  
Bayes Estimators ◽  
Monte Carlo Simulation Study ◽  
Highest Posterior Density ◽  
Censoring Scheme

In this paper, a first-failure progressive hybird censoring scheme is introduced that combines progressive first-failure censoring and Type-I censoring. We obtain the maximum likelihood estimators (MLEs) and the Bayes estimators of the unknown parameters from the inverse Gaussian distribution based on the first-failure progressive hybird censoring scheme. The Bayes estimates are computed under squared error, Linex and general entropy loss functions. The asymptotic confidence intervals and coverage probabilities for the parameters are obtained based on the observed Fisher?s information matrix. Also, highest posterior density credible intervals for the parameters are computed using Gibbs sampling procedure. A Monte Carlo simulation study is conducted in order to compare the Bayes estimators with the MLEs. Real life data sets are provided to illustration purposes.

scholarly journals Designing of Repetitive Group Sampling Plan under the Inverse Gaussian Distribution

2019 ◽  
Vol 9 (1S4) ◽  
pp. 987-991
Keyword(s):  
Gaussian Distribution ◽  
Shape Parameter ◽  
Gaussian Model ◽  
Inverse Gaussian Distribution ◽  
Design Parameters ◽  
Inverse Gaussian ◽  
Shape Parameters ◽  
Sampling Plans ◽  
Truncated Life Test ◽  
Repetitive Group Sampling

This study investigates the attributes repetitive group sampling plans based on a truncated life test under the inverse Gaussian distribution with known shape parameter. The sample size and two acceptance numbers are the three design parameters determined for the proposed repetitive group sampling plans for different mean ratios. Tables are constructed to determine the optimal design parameters for different values of shape parameters of the inverse Gaussian model and the results are explained by with examples. Also the effect of misspecification of shape parameters is also discussed.

scholarly journals Efficient transformed ratio-type estimator using single auxiliary information

2021 ◽  
Vol 48 (2) ◽  
Author(s):  
Sana Amjad ◽  
◽  
Muhammad Ismail ◽  
Keyword(s):  
Mean Squared Error ◽  
Order Approximation ◽  
Real Life ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Variance Estimators ◽  
Life Data ◽  
Squared Error ◽  
Real Life Data ◽  
Better Than

This paper provides an efficient transformed ratio-type estimator to estimate the study variable's population variance by utilizing information of a single auxiliary variable under simple random sampling without replacement. The bias and mean squared error of the proposed estimator are derived up-to 1st order approximation. In addition to this, the efficiency comparison of the proposed estimator has been done with traditional ratio-type variance estimator and some other widely used modified ratio-type variance estimators by taking real-life data. A simulation study has also been carried out to see the performance of the proposed estimator. It is worth noticing that our proposed estimator performs better than the competing estimators in real-life data applications as the mean squared error and root mean squared error of our proposed estimator are smaller than the competing estimators. Hence, our proposed estimator is better than existing variance estimators.

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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