scholarly journals A Bootstrap Approach to Evaluating the Power of the Houck’s Test for Asymmetry

2013 ◽  
Vol 4 (2) ◽  
pp. 69-73
Author(s):  
Henry de-Graft Acquah
Keyword(s):  
Low Power ◽  
Noise Level ◽  
Null Hypothesis ◽  
Parametric Bootstrap ◽  
Bootstrap Sample ◽  
Power Of The Test ◽  
Bootstrap Simulation ◽  
Bootstrap Approach ◽  
Data Generating Process ◽  
S Model

The power of the conventional Houck’s model of asymmetry is examined via parametric bootstrap simulation. The results of the bootstrap simulations indicate that the Houck’s model has low power in rejecting the null of symmetric adjustment. The power of the test depends on the bootstrap sample size, level of asymmetry and the amount of noise in the data generating process used in an application. With a small bootstrap sample and large noise level, the Houck’s model display low power in rejecting the null hypothesis of symmetry.

scholarly journals A bootstrap approach to evaluating the performance of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in selection of an asymmetric price relationship

2012 ◽  
Vol 57 (2) ◽  
pp. 99-110 ◽  
Author(s):  
Henry Acquah
Keyword(s):  
Parametric Bootstrap ◽  
Information Criterion ◽  
Information Criteria ◽  
Price Transmission ◽  
Bootstrap Sample ◽  
Information Theoretic ◽  
Bootstrap Simulation ◽  
Asymmetric Price Transmission ◽  
Bootstrap Approach ◽  
Price Relationship

This study addresses the problem of model selection in asymmetric price transmission models by combining the use of bootstrap methods with information theoretic selection criteria. Subsequently, parametric bootstrap technique is used to select the best model according to Akaike?s Information Criteria (AIC) and Bayesian Information Criteria (BIC). Bootstrap simulation results indicated that the performances of AIC and BIC are affected by the size of the data, the level of asymmetry and the amount of noise in the model used in the application. This study further establishes that the BIC is consistent and outperforms AIC in selecting the correct asymmetric price relationship when the bootstrap sample size is large.

scholarly journals Using Bootstrap Method to Evaluate the Power of Tests for Non-Linearity in Asymmetric Price Relationship

2013 ◽  
Vol 5 (4) ◽  
pp. 237-241
Author(s):  
Henry De-Graft Acquah
Keyword(s):  
Sample Size ◽  
Bootstrap Method ◽  
Complex Model ◽  
Model Complexity ◽  
Bootstrap Sample ◽  
Power Of The Test ◽  
Data Generating Process ◽  
Price Relationship ◽  
False Null Hypothesis ◽  
The Bootstrap Method

This paper introduces and applies the bootstrap method to compare the power of the test for asymmetry in the Granger and Lee (1989) and Von Cramon-Taubadel and Loy (1996) models. The results of the bootstrap simulations indicate that the power of the test for asymmetry depends on various conditions such as the bootstrap sample size, model complexity, difference in adjustment speeds and the amount of noise in the data generating process used in the application. The true model achieves greater power when compared with the complex model. With small bootstrap sample size or large noise, both models display low power in rejecting the (false) null hypothesis of symmetry.

Testing informative hypotheses in SEM increases power: An illustration contrasting classical hypothesis testing with a parametric bootstrap approach

2011 ◽  
Vol 35 (2) ◽  
pp. 180-190 ◽  
Author(s):  
Rens van de Schoot ◽  
Dagmar Strohmeier
Keyword(s):  
Hypothesis Testing ◽  
Simulation Study ◽  
Null Hypothesis ◽  
Inequality Constraints ◽  
Parametric Bootstrap ◽  
Direct Test ◽  
Bootstrap Procedure ◽  
Null Hypothesis Testing ◽  
Bootstrap Approach ◽  
Classical Hypothesis

In the present paper, the application of a parametric bootstrap procedure, as described by van de Schoot, Hoijtink, and Deković (2010), will be applied to demonstrate that a direct test of an informative hypothesis offers more informative results compared to testing traditional null hypotheses against catch-all rivals. Also, more power can be gained when informative hypotheses are tested directly. In this paper we will (a) compare the results of traditional analyses with the results of this novel methodology; (b) introduce applied researchers to the parametric bootstrap procedure for the evaluation of informative hypotheses; and (c) provide the results of a simulation study to demonstrate power gains when using inequality constraints. We argue that researchers should directly evaluate inequality-constrained hypotheses if there is a strong theory about the ordering of relevant parameters. In this way, researchers can make use of all knowledge available from previous investigations, while also learning more from their data compared to traditional null-hypothesis testing.

An Omnibus Test for Systematic Changes in Judges’ Rankings

1992 ◽  
Vol 17 (1) ◽  
pp. 1-26
Author(s):  
Douglas E. Critchlow ◽  
Joseph S. Verducci
Keyword(s):  
Literary Criticism ◽  
Null Hypothesis ◽  
Statistical Test ◽  
Post Treatment ◽  
Graphical Methods ◽  
Test Results ◽  
Test Statistic ◽  
Omnibus Test ◽  
Null Distributions ◽  
Power Of The Test

Paired rankings arise when each subject in a study independently ranks a set of items, undergoes a treatment, and afterwards ranks the same set of items. For such data, a statistical test is proposed to detect if the subjects’ posttreatment rankings have moved systematically toward some unknown ranking or set of rankings. The null hypothesis for this test is that each subject’s post-treatment ranking is symmetrically distributed about his pretreatment ranking. The exact and asymptotic null distributions of the test statistic are simulated and compared, and the power of the test is studied. Using paired rankings from an experimental course in literary criticism, we also offer some graphical methods for representing such data that help us to interpret the test results.

Confidence intervals for the ratio of two independent Poisson rates: Parametric bootstrap, modified asymptotic, and approximate-estimate approaches

2019 ◽  
Vol 29 (8) ◽  
pp. 2140-2150
Author(s):  
Mahmood Kharrati-Kopaei ◽  
Raziye Dorosti-Motlagh
Keyword(s):  
Confidence Intervals ◽  
Coverage Probability ◽  
Ad Hoc ◽  
Rare Events ◽  
Average Length ◽  
Parametric Bootstrap ◽  
Asymptotic Results ◽  
Approximate Estimate ◽  
Bootstrap Approach ◽  
Estimate Method

We propose four confidence intervals for the ratio of two independent Poisson rates. We apply a parametric bootstrap approach, two modified asymptotic results, and we propose an ad-hoc approximate-estimate method to construct confidence intervals. We justify the correctness of the proposed methods asymptotically in the case of non-rare events (when the Poisson rates are large). We also compare the proposed confidence intervals with some recommended ones in the case of rare events (when the Poisson rates are small) via an extensive simulation study. The results show that the proposed modified asymptotic and the approximate-estimate confidence intervals perform reasonably well in terms of coverage probability and average length.

scholarly journals A modern method of multiple working hypotheses to improve inference in ecology

2020 ◽  
Vol 7 (6) ◽  
pp. 200231 ◽  
Author(s):  
Scott W. Yanco ◽  
Andrew McDevitt ◽  
Clive N. Trueman ◽  
Laurel Hartley ◽  
Michael B. Wunder
Keyword(s):  
Data Collection ◽  
Low Power ◽  
Null Hypothesis ◽  
Effective Means ◽  
Modern Method ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Questionable Research Practices ◽  
Study Designs ◽  
Working Hypotheses

Science provides a method to learn about the relationships between observed patterns and the processes that generate them. However, inference can be confounded when an observed pattern cannot be clearly and wholly attributed to a hypothesized process. Over-reliance on traditional single-hypothesis methods (i.e. null hypothesis significance testing) has resulted in replication crises in several disciplines, and ecology exhibits features common to these fields (e.g. low-power study designs, questionable research practices, etc.). Considering multiple working hypotheses in combination with pre-data collection modelling can be an effective means to mitigate many of these problems. We present a framework for explicitly modelling systems in which relevant processes are commonly omitted, overlooked or not considered and provide a formal workflow for a pre-data collection analysis of multiple candidate hypotheses. We advocate for and suggest ways that pre-data collection modelling can be combined with consideration of multiple working hypotheses to improve the efficiency and accuracy of research in ecology.

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