2 edition of **Robustness of the Hotelling"s T2 Test in the presence of outliers in a related measures setting** found in the catalog.

Robustness of the Hotelling"s T2 Test in the presence of outliers in a related measures setting

Serge GГЎerard Demers

- 318 Want to read
- 13 Currently reading

Published
**2005**
.

Written in English

- Psychometrics.,
- Outliers (Statistics),
- Multivariate analysis.

Several inferential statistics are routinely applied to data without a thorough understanding of the effect of outliers on them. The Hotelling"s T2 test may prove to be inaccurate in the presence of outliers given the test"s dependence on the mean and standard deviation of the data set. This study examined the performance of the Hotelling"s T2 test in terms of Type I error rate and power and contrasted its performance with a robust version of the Hotelling"s T2 test as well as an outlier detection and removal method. The goal of the study was to determine the impact of (1) the sample size, (2) the contamination rate, (3) the alpha level, (4) the number of variates, and (5) the structure of the outliers on all three methods. Data for this repeated measures study were simulated based on a real educational data set where outliers were added. Robustness of Type I error rates and power for Hotelling"s T2 was demonstrated for all of the contamination patterns and sample sizes used in the study. The robust T2 test produced good results for the larger sample sizes but generally non-robust results for small sample sizes as well as for small alpha levels. The outlier removal method produced better results than the robust T2 in situations where the sample sizes were small. Results suggest that the Hotelling"s T2 test is the most stable and most robust of the three methods under the conditions of this study.

**Edition Notes**

Statement | by Serge GâerardDemers. |

The Physical Object | |
---|---|

Pagination | xv, 222 leaves. |

Number of Pages | 222 |

ID Numbers | |

Open Library | OL19515161M |

ISBN 10 | 0494076216 |

$\begingroup$ Statistically rank-based tests are robust against outlier. But an outlier is an outlier, on the operational level the analyst should still examine that case. So, I'd say we still need to "partially" worry about outliers. $\endgroup$ – Penguin_Knight Sep 25 '13 at This skill test was designed to test your conceptual and practical knowledge of various regression techniques. A total of number of people participated in the test. I am sure they all will agree it was the best skill assessment test on regression they have come across.

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The influence of outliers in one of the samples on Hotelling's generalized T 1 0 is investigated by considering the directional derivate of the expected value with respect to the proportion of outliers in the disturbed population at the proportion zero.

To make the problem tractable, the estimate of the covariance matrix thereby is replaced by its expected value under the altered Cited by: 6. •T o investigate the robustness of the Hotelling’s T2 test, a robust version of the test, and an outlier detection and removal strategy in the presence of various types and quantities of outliers.

• Willems et al. () contend that the traditional Hotelling’s T2 test suffers badly from the effect of outliers.

Alternate Report Number: OLK NSF For Students. where the \(s_a^2\) values are constants, and are the variances of each component. The easiest interpretation is that \(T^2\) is a scalar number that summarizes all the score values. Some other properties regarding \(T^2\).

It is a positive number, greater than or equal to zero. It is the distance from the center of the (hyper)plane to the projection of the observation onto the (hyper)plane.

Hotelling's T 2 test (Hotelling, ) is the multivariate generlisation of the Student's t test; however, objects subject to a Hotelling's T 2 should be described by multiple response variables.

A one-sample Hotelling's T 2 test can be used to test if a set of objects (which should be a sample of a single statistical population) has a mean equal to a hypothetical mean (Figure 1a).

In the retrospective stage (Phase I) analysis of a historical data set, the presence of multiple outliers may go undetected by the conventional T2 chart, due to masking. Hotelling's T2 test has been mainly used in and robustness measures. The performances of the proposed robust T2 control chart and the classical and the M estimators are also compared by means.

Sample T2 Introduction The two-sample Hotelling’s T2 is the multivariate extension of the common two-group Student’s t-test. In a t-test, differences in the mean response between two populations are studied.

T2 is used when the number of response variables are two or more, although it can be used when there is only one response variable. The final quantity from a PCA model that we need to consider is called Hotelling’s \(T^2\) value. A classic method is the Hotelling T2 test statistic T2 = nX¯ Tˆ −1X¯ where X¯ is the sample mean vector and ˆ is the sample covariance matrix.

Robust Hotelling T2 test. Theory, assumptions, and interpretation of the Hotelling T2 test. In this paper, focused on selecting the most sensitive chart in detecting outliers by comparing signal probability of each method. The results showed that MVE chart has the highest signal probability in uncorrelated data and WD chart is robust in correlated data.

Index Terms — Multivariate control chart, Outliers, Robust, Signal probability. In DescTools: Tools for Descriptive Statistics. Description Usage Arguments Details Value Author(s) References Examples. Description. Hotelling's T2 test is the multivariate generlisation of the Student's t test. A one-sample Hotelling's T2 test can be used to test if a set of vectors of data (which should be a sample of a single statistical population) has a mean equal to a hypothetical mean.

Hotelling’s T2 Test Section Parametric Randomization Test Test Prob Prob Hypothesis T2 DF1 DF2 Level Level Means All Zero 3 Means All Equal 2 The randomization test results are based on Monte Carlo samples.

This report gives the results of the two T2 tests. Hypothesis. Abstract. The Hotelling statistic is the most popular statistic used in multivariate control charts to monitor multiple qualities.

However, this statistic is easily affected by the existence of more than one outlier in the data set. To rectify this problem, robust control charts, which are based on the minimum volume ellipsoid and the minimum covariance determinant, have been proposed.

ii Abstract Hotelling's T2 chart is commonly used for Phase I analysis of individual multivariate normally distributed data. However, the presence of only a few outliers can significantly distort classical estimates of location and scale, thus rendering the resulting analysis ineffective.

How robust is the independent samples t-test when the distributions of the samples are non-normal. 4 Generating null distributions by a residual permutation procedure. Multivariate control charts are widely used in practice to monitor the simultaneous performance of several related quality characteristics.

The origin of multivariate control chart can be attributed to Hotelling [1]. Compute an F-Statistic: Convert T^2 statistic to F-values and compute an F-test to indicate whether there's a separation between the clusters. Perform an hypothesis test using the F-statistic: If the F-value is greater than the critical F-value, then the null hypothesis, which assumes that there is no separation between the groups, can be rejected.

Sheskin () states that the presence of one or several outliers can have a substantial effect on both the mean and the variance of a distribution. If the variance is impacted by outliers test-statistics might not be reliable.

Zimmerman & Zumbo () discuss provide the following definition in their book titled “Outliers in Statistical. The reader can think of airplane crashes as outliers of failure in the same way that people like Bill Gates are successful outliers.

They are rare, and when they do happen, it is because of a confluence of various seemingly unrelated factors. When reporting any test that would lead to a statistically significant result (either the test with inclusion or exclusion of outliers (or both)), in between % and % of the independent sample t-tests were statistically significant, and for the repeated measures ANOVA design this was between % and % of the tests.

The classical estimatorsare usually used to estimate these two parameters. They aredefined based on assumptions which are not always weakness of the classical estimators is their sensitivityto the presence of outliers.

One way to deal with outliers isto use robust estimators. In this study, a robust Hotelling T2control chart is proposed.ICSNP provides Hotellings T2 test as well as a range of non-parametric tests including location tests based on marginal ranks, spatial median and spatial signs computation, estimates of shape.

Non-parametric two sample tests are also available from cramer and spatial sign and rank tests to investigate location, sphericity and independence are.Hockey team in first chapter of the book. One of the two finest teams in the Canadian Hockey League. Roster of the team is presented and pattern of players born in certain months; most players born in January, February, or March because of cutoff dates for all teams beginning at a young age.