Related Research Articles
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance,where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form,ANOVA provides a statistical test of whether two or more population means are equal,and therefore generalizes the t-test beyond two means. In other words,the ANOVA is used to test the difference between two or more means.
In statistics,an outlier is a data point that differs significantly from other observations. An outlier may be due to a variability in the measurement,an indication of novel data,or it may be the result of experimental error;the latter are sometimes excluded from the data set. An outlier can be an indication of exciting possibility,but can also cause serious problems in statistical analyses.
Cross-validation,sometimes called rotation estimation or out-of-sample testing,is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set. Cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations. It is mainly used in settings where the goal is prediction,and one wants to estimate how accurately a predictive model will perform in practice. In a prediction problem,a model is usually given a dataset of known data on which training is run,and a dataset of unknown data against which the model is tested. The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it,in order to flag problems like overfitting or selection bias and to give an insight on how the model will generalize to an independent dataset.
The Kruskal–Wallis test by ranks,Kruskal–Wallis H test,or one-way ANOVA on ranks is a non-parametric method for testing whether samples originate from the same distribution. It is used for comparing two or more independent samples of equal or different sample sizes. It extends the Mann–Whitney U test,which is used for comparing only two groups. The parametric equivalent of the Kruskal–Wallis test is the one-way analysis of variance (ANOVA).
In statistics,the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary.
In statistics,resampling is the creation of new samples based on one observed sample. Resampling methods are:
- Permutation tests
- Bootstrapping
- Cross validation
MedCalc is a statistical software package designed for the biomedical sciences. It has an integrated spreadsheet for data input and can import files in several formats.
In statistics,the false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the FDR,which is the expected proportion of "discoveries" that are false. Equivalently,the FDR is the expected ratio of the number of false positive classifications to the total number of positive classifications. The total number of rejections of the null include both the number of false positives (FP) and true positives (TP). Simply put,FDR = FP /. FDR-controlling procedures provide less stringent control of Type I errors compared to family-wise error rate (FWER) controlling procedures,which control the probability of at least one Type I error. Thus,FDR-controlling procedures have greater power,at the cost of increased numbers of Type I errors.
In statistics,family-wise error rate (FWER) is the probability of making one or more false discoveries,or type I errors when performing multiple hypotheses tests.
In statistics,stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure. In each step,a variable is considered for addition to or subtraction from the set of explanatory variables based on some prespecified criterion. Usually,this takes the form of a forward,backward,or combined sequence of F-tests or t-tests.
Omnibus tests are a kind of statistical test. They test whether the explained variance in a set of data is significantly greater than the unexplained variance,overall. One example is the F-test in the analysis of variance. There can be legitimate significant effects within a model even if the omnibus test is not significant. For instance,in a model with two independent variables,if only one variable exerts a significant effect on the dependent variable and the other does not,then the omnibus test may be non-significant. This fact does not affect the conclusions that may be drawn from the one significant variable. In order to test effects within an omnibus test,researchers often use contrasts.
In statistics,the Bonferroni correction is a method to counteract the multiple comparisons problem.
In statistics,the multiple comparisons,multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values.
In statistics,the closed testing procedure is a general method for performing more than one hypothesis test simultaneously.
The Newman–Keuls or Student–Newman–Keuls (SNK)method is a stepwise multiple comparisons procedure used to identify sample means that are significantly different from each other. It was named after Student (1927),D. Newman,and M. Keuls. This procedure is often used as a post-hoc test whenever a significant difference between three or more sample means has been revealed by an analysis of variance (ANOVA). The Newman–Keuls method is similar to Tukey's range test as both procedures use studentized range statistics. Unlike Tukey's range test,the Newman–Keuls method uses different critical values for different pairs of mean comparisons. Thus,the procedure is more likely to reveal significant differences between group means and to commit type I errors by incorrectly rejecting a null hypothesis when it is true. In other words,the Neuman-Keuls procedure is more powerful but less conservative than Tukey's range test.
Anil K. Bera is an Indian-American econometrician. He is Professor of Economics at University of Illinois at Urbana–Champaign's Department of Economics. He is most noted for his work with Carlos Jarque on the Jarque–Bera test.
Yoav Benjamini is an Israeli statistician best known for development of the “false discovery rate”criterion. He is currently The Nathan and Lily Silver Professor of Applied Statistics at Tel Aviv University.
In statistics,linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables. The case of one explanatory variable is called simple linear regression;for more than one,the process is called multiple linear regression. This term is distinct from multivariate linear regression,where multiple correlated dependent variables are predicted,rather than a single scalar variable.
References
- 1 2 3 4 "Ajit Tamhane".
- ↑ "Ajit Tamhane - Google Scholar".
- ↑ "American Statistical Association - Fellows".
- 1 2 "Honored IMS Fellows".
- ↑ "Professor Ajit C. Tamhane named Fellow of AAAS".
- ↑ "International Statistical Institute - Individual Members". Archived from the original on 2021-10-05. Retrieved 2021-08-06.
- ↑ Holt, Melinda Miller (2001). "Statistics and Data Analysis From Elementary to Intermediate". Technometrics. 43 (2): 237–238. doi:10.1198/tech.2001.s590. S2CID 36333639.
- ↑ Predictive Analytics: Parametric Models for Regression and Classification Using R. ASIN 1118948890.
- ↑ Peritz, Eric (1989). "Book Reviews: Multiple Comparison Procedures Y. Hochberg and A. C. Tamhane New York: Wiley, 1987. xxii + 450 pp". Journal of Educational Statistics. 14: 103–106. doi:10.3102/10769986014001103. S2CID 120709053.
- ↑ {{cite journal|url=https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.201210003%7Ctitle=Multiple Testing Problems in Pharmaceutical Statistics. Dmitrienko, A., Tamhane, A. C., and Bretz, F. (2010). Boca Raton: Chapman & Hall/CRC Biostatistics Series. {{|978-1-584 88984-7}}|journal=Biometrical Journal |date=July 2012 |volume=54 |issue=4 |pages=579–580 |doi=10.1002/bimj.201210003 |last1=Konietschke |first1=Frank }}
- ↑ Tamhane, Ajit C.; Logan, Brent R. (2002). "Multiple Test Procedures for Identifying the Minimum Effective and Maximum Safe Doses of a Drug". Journal of the American Statistical Association. 97 (457): 293–301. doi:10.1198/016214502753479428. S2CID 18790850.
- ↑ Tamhane, Ajit C.; Wu, Yi; Mehta, Cyrus R. (2012). "Adaptive extensions of a two-stage group sequential procedure for testing primary and secondary endpoints (II): sample size re-estimation". Statistics in Medicine. 31 (19): 2041–2054. doi:10.1002/sim.5377. PMID 22733687. S2CID 13044876.
- ↑ Dmitrienko, Alex; Kordzakhia, George; Tamhane, Ajit C. (2011). "Multistage and Mixture Parallel Gatekeeping Procedures in Clinical Trials". Journal of Biopharmaceutical Statistics. 21 (4): 726–747. doi:10.1080/10543406.2011.551333. PMID 21516566. S2CID 14291397.
- ↑ Peritz, Eric (1989). "Book Reviews: Multiple Comparison Procedures Y. Hochberg and A. C. Tamhane New York: Wiley, 1987. xxii + 450 pp". Journal of Educational Statistics. 14: 103–106. doi:10.3102/10769986014001103. S2CID 120709053.
- ↑ "Two-stage selection procedures with attention to screening" (PDF).
- ↑ "Incomplete Block Designs for Comparing Treatments with a Control: General Theory" (PDF).
- ↑ Malthouse, E.C.; Tamhane, A.C.; Mah, R.S.H. (1997). "Nonlinear partial least squares". Computers & Chemical Engineering. 21 (8): 875–890. doi:10.1016/S0098-1354(96)00311-0.
- ↑ Iordache, C.; Mah, R. S. H.; Tamhane, A. C. (1985). "Performance studies of the measurement test for detection of gross errors in process data". AIChE Journal. 31 (7): 1187–1201. doi:10.1002/aic.690310717.
- ↑ "A Bayesian Approach to Gross Error Detection in Chemical Process Data" (PDF).
- ↑ Tamhane, Ajit C.; Mah, Richard S. H. (1985). "Data Reconciliation and Gross Error Detection in Chemical Process Networks". Technometrics. 27 (4): 409–422. doi:10.1080/00401706.1985.10488080.
- ↑ [[[List of fellows of the American Statistical Association#1991]] "List of fellows of the American Statistical Association"].
{{cite web}}: Check|url=value (help) - ↑ "Professor Ajit C. Tamhane".
| International | |
|---|---|
| National | |
| Academics | |
| Other | |
