Abstract
Multiple comparison procedures are frequently used to test differences in all possible pairs of means after analysis ofvariance (ANOVA). Detailed information about the difference between means is derived by using multiple comparisontests. Multiple hypothesis testing plays a pivotal role in analyzing data from modern scientific investigations in healthresearch, especially. Many different tests have been suggested. The present article reviews of false discovery rateprocedures. The false discovery rate is a multiple hypothesis testing quantity that describes the expected proportion offalse positive results among all rejected null hypotheses. This is a distribution free method that controls the expectedfraction of falsely rejected null hypotheses among those rejected. The review was motivated by examples of falsediscovery rate practices. The aim of this study is to introduce the false discovery rate procedures to the researchers.
References
- Cribbie RA, Keselman HJ. Pairwise Multiple Comparisons: A Model Comparison Approach Versus Stepwise Procedures. British Journal of Mathematical and Statistical Psychology. 2003; 56:167-82.
- Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. Journal of Royal Statistical Society B Series. 1995; 57(1):289-300.
- Soriç B. Statistical “Discoveries” and Effect-Size Estimation. Journal of The American Statistical Association. 1989; 84:608-10.
- Benjamini Y, Hochberg Y. On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics. Journal of Educational and Behavioral Statistics. 2000; 25(1):60-83.
- Storey JD, Taylor JE, Siegmund D. Strong Control, Conservative Point Estimation and Simultaneous Conservative Consistency of False Discovery Rate: a Unified Approach. Journal of Royal Statistical Society B Series. 2004; 66(1):187-205.
- Efron B, Tibshirani RJ, Storey JD, Tusher V. Empirical Bayes Analysis of a Microarray Experiment. Journal of The American Statistical Association. 2001; 96:1151-60.
- Storey JD. A Direct Approach to False Discovery Rates. Journal of Royal Statistical Society B Series. 2002; 64:479-98.
- Benjamini Y, Krieger AM, Yekutieli D. Adaptive Linear Step-up Procedures that Control the False Discovery Rate. Biometrika. 2006; 93(3):491-507.
- Black MA. A Note on the Adaptive Control of False Discovery Rates. Journal of Royal Statistical Society B Series. 2004; 66(2):297-304.
Abstract
Varyans Analizi, ikiden çok ortalamanın karşılaştırılmasında, ortalamalar arasında fark olup olmadığını belirlemekte, ancak hangi ortalamanın hangi ortalamadan farklı olduğunu belirleyememektedir. Çoklu karşılaştırma işlemleri ise ortalamalar arasındaki farklılıklar hakkında çok daha detaylı bilgi vermektedir. Çoklu hipotez testleri, özellikle sağlık alanında yapılan modern bilimsel araştırmalardan elde edilen verilerin analizinde kilit rol oynamaktadır. Bu amaçla çok sayıda çoklu karşılaştırma testi önerilmiştir. Çalışmada yanlış bulgu oranı yaklaşımları gözden geçirilmiştir. Yanlış bulgu oranı, çoklu hipotez testlerinde reddedilen yokluk hipotezleri arasında yanlışlıkla reddedilen hipotezlerin beklenen oranını ifade eden bir değerdir. Bu çalışmada amaç, literatürde yer alan yanlış bulgu oranı yaklaşımları ile ilgili örnekler vererek son yıllarda daha yoğun olarak kullanılmaya başlanan ancak araştırmacılar tarafından daha az bilinen yanlış bulgu oranı yaklaşımı ile ilgili işlemleri tanıtmaktır.
References
- Cribbie RA, Keselman HJ. Pairwise Multiple Comparisons: A Model Comparison Approach Versus Stepwise Procedures. British Journal of Mathematical and Statistical Psychology. 2003; 56:167-82.
- Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. Journal of Royal Statistical Society B Series. 1995; 57(1):289-300.
- Soriç B. Statistical “Discoveries” and Effect-Size Estimation. Journal of The American Statistical Association. 1989; 84:608-10.
- Benjamini Y, Hochberg Y. On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics. Journal of Educational and Behavioral Statistics. 2000; 25(1):60-83.
- Storey JD, Taylor JE, Siegmund D. Strong Control, Conservative Point Estimation and Simultaneous Conservative Consistency of False Discovery Rate: a Unified Approach. Journal of Royal Statistical Society B Series. 2004; 66(1):187-205.
- Efron B, Tibshirani RJ, Storey JD, Tusher V. Empirical Bayes Analysis of a Microarray Experiment. Journal of The American Statistical Association. 2001; 96:1151-60.
- Storey JD. A Direct Approach to False Discovery Rates. Journal of Royal Statistical Society B Series. 2002; 64:479-98.
- Benjamini Y, Krieger AM, Yekutieli D. Adaptive Linear Step-up Procedures that Control the False Discovery Rate. Biometrika. 2006; 93(3):491-507.
- Black MA. A Note on the Adaptive Control of False Discovery Rates. Journal of Royal Statistical Society B Series. 2004; 66(2):297-304.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Research Articles |
| Authors | |
| Publication Date | January 13, 2014 |
| Submission Date | October 1, 2013 |
| Published in Issue | Year 2013 Volume: 3 Issue: 3 |
