Bootstrap versus traditional hypothesis testing procedures for coefficients in least absolute value regression

2002 ◽  
Vol 72 (8) ◽  
pp. 665-675 ◽  
Author(s):  
Terry Dielman ◽  
Elizabeth Rose
Keyword(s):  
Hypothesis Testing ◽  
Absolute Value ◽  
Testing Procedures ◽  
Least Absolute Value

Comparison of methods to account for autocorrelation in correlation analyses of fish data

1998 ◽  
Vol 55 (9) ◽  
pp. 2127-2140 ◽  
Author(s):  
Brian J Pyper ◽  
Randall M Peterman
Keyword(s):  
Monte Carlo ◽  
Hypothesis Testing ◽  
Type I Error ◽  
Low Frequency ◽  
Error Rates ◽  
Type I ◽  
Testing Procedures ◽  
Type I Error Rates ◽  
Fish Recruitment ◽  
Correlation Analyses

Autocorrelation in fish recruitment and environmental data can complicate statistical inference in correlation analyses. To address this problem, researchers often either adjust hypothesis testing procedures (e.g., adjust degrees of freedom) to account for autocorrelation or remove the autocorrelation using prewhitening or first-differencing before analysis. However, the effectiveness of methods that adjust hypothesis testing procedures has not yet been fully explored quantitatively. We therefore compared several adjustment methods via Monte Carlo simulation and found that a modified version of these methods kept Type I error rates near . In contrast, methods that remove autocorrelation control Type I error rates well but may in some circumstances increase Type II error rates (probability of failing to detect some environmental effect) and hence reduce statistical power, in comparison with adjusting the test procedure. Specifically, our Monte Carlo simulations show that prewhitening and especially first-differencing decrease power in the common situations where low-frequency (slowly changing) processes are important sources of covariation in fish recruitment or in environmental variables. Conversely, removing autocorrelation can increase power when low-frequency processes account for only some of the covariation. We therefore recommend that researchers carefully consider the importance of different time scales of variability when analyzing autocorrelated data.

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