scholarly journals Bootstrap Confidence Intervals

2021 ◽  
pp. 189-216
Keyword(s):  
Confidence Intervals ◽  
Bootstrap Confidence Intervals

Two-Condition Within-Participant Statistical Mediation Analysis: A Path-Analytic Framework

2019 ◽  
Author(s):  
Amanda Kay Montoya ◽  
Andrew F. Hayes
Keyword(s):  
Confidence Intervals ◽  
Indirect Effect ◽  
Mediation Analysis ◽  
Analytic Approach ◽  
Analytic Framework ◽  
Hypothesis Tests ◽  
Bootstrap Confidence Intervals ◽  
Complex Models ◽  
Path Analytic ◽  
Statistical Mediation Analysis

Researchers interested in testing mediation often use designs where participants are measured on a dependent variable Y and a mediator M in both of two different circumstances. The dominant approach to assessing mediation in such a design, proposed by Judd, Kenny, and McClelland (2001), relies on a series of hypothesis tests about components of the mediation model and is not based on an estimate of or formal inference about the indirect effect. In this paper we recast Judd et al.’s approach in the path-analytic framework that is now commonly used in between-participant mediation analysis. By so doing, it is apparent how to estimate the indirect effect of a within-participant manipulation on some outcome through a mediator as the product of paths of influence. This path analytic approach eliminates the need for discrete hypothesis tests about components of the model to support a claim of mediation, as Judd et al’s method requires, because it relies only on an inference about the product of paths— the indirect effect. We generalize methods of inference for the indirect effect widely used in between-participant designs to this within-participant version of mediation analysis, including bootstrap confidence intervals and Monte Carlo confidence intervals. Using this path analytic approach, we extend the method to models with multiple mediators operating in parallel and serially and discuss the comparison of indirect effects in these more complex models. We offer macros and code for SPSS, SAS, and Mplus that conduct these analyses.

Bootstrap Confidence Intervals of the Modified Process Capability Index for Weibull distribution

2017 ◽  
Vol 42 (11) ◽  
pp. 4565-4573 ◽  
Author(s):  
Muhammad Kashif ◽  
Muhammad Aslam ◽  
G. Srinivasa Rao ◽  
Ali Hussein AL-Marshadi ◽  
Chi-Hyuck Jun
Keyword(s):  
Weibull Distribution ◽  
Confidence Intervals ◽  
Process Capability ◽  
Process Capability Index ◽  
Bootstrap Confidence Intervals ◽  
Capability Index

scholarly journals LINK OF MOMENTS BEFORE AND AFTER TRANSFORMATIONS, WITH AN APPLICATION TO RESAMPLING FROM FAT-TAILED DISTRIBUTIONS

2018 ◽  
Vol 35 (03) ◽  
pp. 630-652 ◽  
Author(s):  
Karim M. Abadir ◽  
Adriana Cornea-Madeira
Keyword(s):  
Finite Number ◽  
Confidence Intervals ◽  
Characteristic Functions ◽  
Infinite Variance ◽  
Fundamental Relation ◽  
Bootstrap Confidence Intervals ◽  
Finite Samples ◽  
Before And After ◽  
The Mean ◽  
Do So

Let x be a transformation of y, whose distribution is unknown. We derive an expansion formulating the expectations of x in terms of the expectations of y. Apart from the intrinsic interest in such a fundamental relation, our results can be applied to calculating E(x) by the low-order moments of a transformation which can be chosen to give a good approximation for E(x). To do so, we generalize the approach of bounding the terms in expansions of characteristic functions, and use our result to derive an explicit and accurate bound for the remainder when a finite number of terms is taken. We illustrate one of the implications of our method by providing accurate naive bootstrap confidence intervals for the mean of any fat-tailed distribution with an infinite variance, in which case currently available bootstrap methods are asymptotically invalid or unreliable in finite samples.

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