Results from exploratory three-way analysis techniques such as CANDECOMP/PARAFAC and Tucker3 analysis are usually presented without giving insight into uncertainties due to sampling. Here a bootstrap procedure is proposed that produces percentile intervals for all output parameters. Special adjustments are offered for handling the non-uniqueness of the solutions. The percentile intervals indicate the instability of the sample solutions. By means of a simulation study it is demonstrated that the percentile intervals can fairly well be interpreted as confidence intervals for the output parameters. Copyright (C) 2004 John Wiley Sons, Ltd
This paper describes some theoretical backgrounds of confidence interval construction. The order of ...
This paper considers the problem of choosing the number of bootstrap repetitions B for bootstrap sta...
An extension of Monte Carlo methods to confidence interval estimation, using the bootstrap technique...
Results from exploratory three-way analysis techniques such as CANDECOMP/PARAFAC and Tucker3 analysi...
The two most common component methods for the analysis of three-way data, CANDECOMP/PARAFAC (CP) and...
A bootstrap method for generating confidence intervals in linear models is suggested. The method is ...
This article investigates the bootstrap methods for producing good approximate confidence intervals....
Nonparametric techniques provide no analytical solutions for confidence intervals. The bootstrap and...
Abstract: This article evaluates methods of computing confidence intervals and values of descriptive...
Quantiles and percentiles represent useful statistical tools for describing the distribution of resu...
A linear method for the construction of asymptotic bootstrap confidence intervals is proposed. We ap...
1. Researchers often want to place a confidence interval around estimated parameter values calculate...
<p>Schematic of the bootstrap process. We want to estimate a confidence interval for the phase <i>θ<...
The following paper details how the use of simulation can help to introduce computer intensive appli...
It is widely known that bootstrap failure can often be remedied by using a technique known as the 'm...
This paper describes some theoretical backgrounds of confidence interval construction. The order of ...
This paper considers the problem of choosing the number of bootstrap repetitions B for bootstrap sta...
An extension of Monte Carlo methods to confidence interval estimation, using the bootstrap technique...
Results from exploratory three-way analysis techniques such as CANDECOMP/PARAFAC and Tucker3 analysi...
The two most common component methods for the analysis of three-way data, CANDECOMP/PARAFAC (CP) and...
A bootstrap method for generating confidence intervals in linear models is suggested. The method is ...
This article investigates the bootstrap methods for producing good approximate confidence intervals....
Nonparametric techniques provide no analytical solutions for confidence intervals. The bootstrap and...
Abstract: This article evaluates methods of computing confidence intervals and values of descriptive...
Quantiles and percentiles represent useful statistical tools for describing the distribution of resu...
A linear method for the construction of asymptotic bootstrap confidence intervals is proposed. We ap...
1. Researchers often want to place a confidence interval around estimated parameter values calculate...
<p>Schematic of the bootstrap process. We want to estimate a confidence interval for the phase <i>θ<...
The following paper details how the use of simulation can help to introduce computer intensive appli...
It is widely known that bootstrap failure can often be remedied by using a technique known as the 'm...
This paper describes some theoretical backgrounds of confidence interval construction. The order of ...
This paper considers the problem of choosing the number of bootstrap repetitions B for bootstrap sta...
An extension of Monte Carlo methods to confidence interval estimation, using the bootstrap technique...