
Review of Short Phrases and Links 
This Review contains major "Test Statistics" related terms, short phrases and links grouped together in the form of Encyclopedia article.
Definitions
 The test statistics are compared with the t distribution on n  2 (sample size  number of regression coefficients) degrees of freedom [ 4].
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 Test statistics are given by LjungBoxPierce portmanteau tests on the residuals and the squared residuals.
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 Test statistics are computed with the TEST command that immediately follows the estimation command.
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 Two test statistics are proposed for testing the equality of two correlated proportions when some observations are missing on both responses.
 The test statistics are very easy to compute, and their asymptotic distributions are simple.
 It can also be used in the formulation of test statistics, such as the Wald test.
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 In addition, different tests use different techniques for partitioning the error term in calculating the test statistics.
 The kstest, kstest2, and lillietest functions compute test statistics that are derived from the empirical cdf.
 We study five such test statistics, which include BrownForsythe test statistic and Welch test statistic.
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 The distribution of the test statistics in the SOWH, SSOWH, SDNB, and SDPB tests were built based on 100 bootstrap replicates.
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 The code can be easily modified to handle other kinds of structural zeros and other test statistics.
 In all such approaches, the stochastic dependence between gene expression values or test statistics is a nuisance that hinders their application.
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 Next, asymptotic expansions of one and twoway test statistics are obtained by using this general one.
 All the twoway test statistics described in this section test the null hypothesis of no association between the row variable and the column variable.
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 Analytical expressions for the test statistics and the required derivatives are provided.
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 The appropriate percentile of the distribution of the resulting test statistics can be taken to be the critical value.
 The corrected sampling variogram for BreX's bonanza borehole, its primary data set and test statistics are posted on this page.
 This paper also outlines principles for calculating Monte Carlo p values for generalized test statistics.
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 The stochastic dependence between expression levels and thus between the associated test statistics is really a serious problem.
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 Yekutieli, D. and Benjamini, Y. (1999). Resamplingbased false discovery rate controlling multiple test procedures for correlated test statistics.
 Different problems have different patterns of structural zeros and different test statistics.
 When analyzing real world biological data sets, normalization procedures are unable to completely remove correlation between the test statistics.
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 Simple estimation and test statistics may not be sufficient for adequate interpretation of the effects in an analysis.
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 As an example, asymptotics of test statistics in the analysis of covariance structures are discussed in detail.
 The bounds are valid under general and unknown dependence structures between the test statistics.
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 Default is 'FALSE'. get.cutoff Logical indicating whether to compute thresholds for the test statistics.
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 Most of the test statistics available in the literature were difficult to compute even with the help of the computer.
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 Returns a vector of 01 values, with a 1 for each test statistics which is twosided.
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 The C&G Procedure utilizes both of the test statistics and gives a practical way for identifying significant periodic genes in massive microarray data.
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 This procedure controls the FDR for both of independent and positively dependent test statistics (Benjamini and Liu 1999; Benjamini and Yekutieli 2001).
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 Randomisation tests are useful with standard test statistics (e.g.
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 Furthermore, each replicate consists of the calculation of a large number of test statistics.
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 To account for the pairing we drop the within pair distance in our test statistics since it will be systematically less than the between pair distances.
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 Such correlation is not usually taken into account by standard test statistics.
 For example, when there are 4 treatements and 6 subjects per treatment, there are 20 degrees of freedom for the various test statistics.
 You are of course free to choose other test statistics to employ within the permutation test.
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 In this case, the test statistics reject the noautocorrelation hypothesis at a high level of significance.
 Two test statistics, which are based on the difference and ratio of rates, consistently outperformed the other measures.
 The difference in the two regression test statistics, in or out of the test sample, is approximately equal to twice the log LR statistic.
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 This paper discusses asymptotic expansions for the null distributions of some test statistics for profile analysis under nonnormality.
 In this paper, a new stepdown procedure is presented, and it also controls the FDR when the test statistics are independent.
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 The test statistics introduced in this paper incorporate the population size directly into the relative or absolute comparison of rates.
 Their estimated standard deviations are listed in the next column followed by the test statistics to determine whether or not each parameter is zero.
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 Set to TRUE to compute test statistics using tests specified in conTest and catTest.
 To investigate the behavior of these test statistics for a variety of situations, we applied these statistical tests to many simulated datasets.
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 The first caveat is that these test statistics are computed under the assumption of joint multivariate normality.
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 Bahadur, R.R. (1967). Rates of convergence of estimates and test statistics.
 In practice, one must choose a method for estimating the test statistics null distribution.
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 Benjamini and Yekutieli (BY) [ 4] proposed a new FDR procedure considering a certain dependency structure among the test statistics.
 Many test statistics have been proposed specifically for this case, yet remarkably the power of these methods has not previously been compared.
 Other test statistics for searching "hidden periodicity" in a time series have been proposed as part of spectral analysis (Fuller [ 21]) in the literature.
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 Results: We developed an efficient Monte Carlo approach to approximating the joint distribution of the test statistics along the genome.
 Useful for comparing multiple X 2 test statistics and is generalizable across contingency tables of varying sizes.
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 For computation, an algorithm for an efficient procedure is proposed to construct the estimates and test statistics.
 In such problem, no correction has to be done except for the total number of tags and our test statistics under model M00 are adapted.
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 We illustrate the performance and applicability of permGPU within the context of permutation resampling for a number of test statistics.
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 For such an infinitely dense map in which markers are located everywhere over the genome, test statistics at nearby intervals are not independent any more.
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 The difficulty is that the sampling distribution of a test statistics is unknown.
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 Both bootstrap and permutation estimators of the test statistics ($t$ or $F$statistics) null distribution are available.
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 Both bootstrap and permutation estimators of the test statistics null distribution are available and can optionally be output to the user.
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 The table will optionally contain test statistics (and P values) comparing the reduction in deviance for the row to the residuals.
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 For this reason, we describe our proposal in terms in p values rather than test statistics.
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 The test statistics G and X 2 can be compared with tabulated values of the Chisquare distribution.
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 Additionally, a sequence of test statistics for joint effects of covariates is displayed.
Categories
 PValues
 Residuals
 Science > Mathematics > Statistics > Distribution
 Science > Mathematics > Statistics > Test Statistic
 Science > Mathematics > Statistics > Null Hypothesis
Related Keywords
* Alternative Hypotheses
* ChiSquare
* Data
* Discrete
* Discrete Data
* Discrete Distributions
* Distribution
* Distributions
* Error Rate
* Hypothesis Testing
* Large Samples
* Linkage Disequilibrium
* Normality
* Null Hypothesis
* Numerical
* PValues
* Parameter Estimates
* Residuals
* Sample Size
* Significance Levels
* Simulations
* Simulation Studies
* Standard Errors
* Statistic
* Statistics
* Test
* Test Statistic
* Value

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