3 edition of **On data snooping and multiple outlier testing** found in the catalog.

On data snooping and multiple outlier testing

- 333 Want to read
- 37 Currently reading

Published
**1984**
by U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Ocean Service, Charting and Geodetic Services, For sale by the National Geodetic Information Center, NOAA in Rockville, Md
.

Written in English

- Nets (Geodesy),
- Multivariate analysis.,
- Least squares.

**Edition Notes**

Statement | Johan J. Kok. |

Series | NOAA technical report NOS -- 30. |

Contributions | United States. National Ocean Service. Office of Charting and Geodetic Services. |

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

Format | Microform |

Pagination | 61 p. |

Number of Pages | 61 |

ID Numbers | |

Open Library | OL17723419M |

Data Snooping. Data Snooping (DS) and other conventional methods use the mean-shift model. In the DS method, it is assumed that only one outlier is present in the observation set. In practice, this method allows detection of more than one outlier and estimation of their locations. DS is performed when the a priori variance of the Cited by: This two-sided test can detect outliers for either the smallest or largest data value, but it has less power than a one-sided test. Smallest data value is an outlier: Use this one-sided test when you suspect that the smallest data value is an outlier. This one-sided test has greater power than a two-sided test, but it cannot detect outliers.

GPS (Global Positioning System) devices can be used in many applications which require accurate point positioning in geosciences. Accuracy of GPS decreases due to outliers resulted from the errors inherent in GPS observations. Several approaches have been developed to detect outliers in geodetic observations. It is important to determine which method is most effective Cited by: by the Cochran test to be an outlier. The test statistic, C,is given by: C ¼ s max 2, Xl i¼1 s i (4) where s max 2 is the suspect repeatability variance and the s i 2 values arethe variances fromall the l participating laboratories. Some collaborative trials utilise only two measurements at each concentration of the analyte in each laboratory.

In effect, you are asking if there is a Stata command that will tell you if values are "too high". If you can translate that into some statistical criterion, then there will be Stata code to do it. In any case, eliminating outliers is a highly debatable tactic. It's just one of several possible actions and in my view usually one of the worst. An outlier is an observation in a set of data that is inconsistent with the majority of the data. An observation (i.e., score) is typically labeled an outlier if it is substantially higher or lower than most of the observations. Because, among other things, the presence of one or more outliers can dramatically alter the values of both the mean and variance of a distribution, it behooves a.

You might also like

Hitch-hiked and other stories

Hitch-hiked and other stories

Cults of Horror

Cults of Horror

challenge of fusion

challenge of fusion

Maintenance and matrimonial property orders and their enforcement.

Maintenance and matrimonial property orders and their enforcement.

Rossini

Rossini

councillor

councillor

Brass anthology

Brass anthology

Dudley

Dudley

Horned lizards

Horned lizards

Talking terrorism

Talking terrorism

The tenth symphony

The tenth symphony

On data snooping and multiple outlier testing. Rockville, Md.: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Ocean Service, Charting and Geodetic Services: For sale by the National Geodetic On data snooping and multiple outlier testing book Center, NOAA, (OCoLC) Material Type: Government publication, National government publication.

Models that can be selected are the null model, which indicates an outlier free set of observations, or a class of alternative models, which contain a set of additional bias parameters.

A common way to select the right model is by using a statistical hypothesis test. In geodesy data snooping is most popular.

Springer, Berlin CrossRef Google Scholar Kok JJ () On data snooping and multiple outlier testing. NOAA Technical Report, NOS NGS.

30, U.S. Department of Commerce, Rockville, Maryland Google Scholar Krakiwsky EJ, Szabo DJ () Development and testing of in-context confidence regions for geodetic survey by: A Robust Method for Multiple Outliers Detection in Multi-ParametricModels Albert K.

Chong San Diego, CA 'Thecritical test values of Baarda's Data Snooping (wJ and Pope'sTAU (TJ were larger than the largest normalized residuals for all the five samples.

The developed method was able to isolate all the outliers from the five samples successfully. A suggestion for future work is to increase the power of the test (success rate) of Iterative Data Snooping procedure by means of a unifying testing procedure relating the iterative Data Snooping.

Stepwise Multiple Testing as Formalized Data Snooping Joseph P. Romano ∗ Department of Statistics Sequoia Hall Stanford University Stanford, CA U.S.A Michael Wolf † Department of Economics and Business Universitat Pompeu Fabra Ramon Trias Fargas, 25–27 Barcelona Spain October ; this revision February Abstract.

This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities.

The chapters of this book can be organized into three categories. Baarda’s outlier test is one of the best established theories in geodetic practice. The optimal test statistic of the local model test for a single outlier is known as the normalized residual. DETECTION OF MULTIPLE OUTLIERS IN UNIVARIATE DATA SETS Marek K.

Solak, PhD Schering-Plough Research Institute, Summit, NJ ABSTRACT A number of methods are available to detect outliers in univariate data sets. Most of these tests are designed to handle one outlier. Kok JJ () On data snooping and multiple outlier testing. In NOAA Technical Report NOS NGS US Department of Commerce, National Geodetic Survey, Rockville, Maryland Google Scholar Lehmann R () Improved critical values for extreme normalized and studentized residuals in Gauss-Markov by: I appreciate the desire to identify multiple outliers with one test, but the reason the tests used target individual values is that the "rejection" criteria depend on its relationship to the statistics of the whole data set.

IF you identify an "outlier" and remove it from your 'legitimate' data set, the statistics of that set change as s: 2.

To facilitate ease of use, the book has been restructured into four parts: Basic Principles, Univariate Data, Multivariate and Structured Data, and Special Topics. This new edition, with the revised and new material, is sure to enhance the book's recognised position as the reference on the subject of by: Also, masking is one reason that trying to apply a single outlier test sequentially can fail.

For example, if there are multiple outliers, masking may cause the outlier test for the first outlier to return a conclusion of no outliers (and so the testing for any additional outliers. Knight N.L., Wang J. and Rizos C.

() Generalised measures of reliability for multiple outliers. J Geod – [14] Koch K.R. () Deviations from the null-hypothesis to be detected by statistical tests. Bull Géod – [15] Kok J.J.

() On data snooping and multiple outlier : Ali Almagbile. So, if the robust Mahalanobis distance is greater than χ 3,1 − α 2 we can consider it as an outlier, i.e., we can define a classical test for outlier detection, with critical region (6) C R = x ∈ ℜ: M D (x) > χ 3,1 − α 2.

Therefore we can apply two outlier detection tests to each sample: the proposed in this paper and that described Cited by: 8. In the conventional methods of Data Snooping (DS), Tau, and t tests, there is a disadvantage since these methods remove outlying baselines which in turn deteriorate the shape of the network.

A normal observation may be detected as an outlier or an outlying observation may be perceived as a normal observation because of the existing outliers in Cited by: I.

Educate yourself on the limitations of statistical inference: Model assumptions, the problems of Types I and II errors, power, and multiple inference, including the "hidden comparisons" that may be involved in data snooping (as in the above example).

Plan your study to take into account the problems involving model assumptions, Type I and II errors, power, multiple inference. On average 80% of the data is fairly accurate; but around 20% of the data is jerky. Plus occasionally we also get an outlier i.e. an erroneous data point far away from the actual trajectory.

I am looking for an algorithm that could me do achieve the following: Smooth out the data so that jerkiness is eliminated. Outlier calculator.

Outliers make statistical analyses difficult. This calculator performs Grubbs' test, also called the ESD method (extreme studentized deviate), to determine whether one of the values in the list you enter is a signficant outlier from the rest. Learn more about the principles of outlier detection and exactly how this test works.

Danuser and M. Stricker, "Parametric Model Fitting: From Inlier Characterization to Outlier Detection," Technical ReportImage Science Lab, ETH Zürich, Google Scholar; G. Danuser, Quantitative Stereo Vision for the Stereo Light Microscope: An Attempt to Provide Control Feedback for a Nanorobot System.

PhD thesis, ETH Zürich. Models that can be selected are the null model, which indicates an outlier free set of observations, or a class of alternative models, which contain a set of additional bias parameters.

A common way to select the right model is by using a statistical hypothesis test. In geodesy data snooping is most popular. Outliers: The Story of Success Paperback – June 7, #N#Malcolm Gladwell (Author) › Visit Amazon's Malcolm Gladwell Page.

Find all the books, read about the author, and more. See search results for this author. Are you an author? Learn about Author Central. Malcolm Gladwell (Author) out of 5 stars 5, ratings/5(K).In statistics, Grubbs's test or the Grubbs test (named after Frank E.

Grubbs, who published the test in ), also known as the maximum normalized residual test or extreme studentized deviate test, is a test used to detect outliers in a univariate data set assumed to come from a normally distributed population.