Applying the Anderson-Darling Test to Suicide Clusters

Crisis ◽  
2013 ◽  
Vol 34 (6) ◽  
pp. 434-437 ◽  
Author(s):  
Donald W. MacKenzie
Keyword(s):  
Poisson Process ◽  
Goodness Of Fit ◽  
Small Samples ◽  
Massachusetts Institute Of Technology ◽  
Homogeneous Poisson Process ◽  
Cornell University ◽  
The Difference ◽  
Suicide Clusters ◽  
Institute Of Technology ◽  
Anderson Darling

Background: Suicide clusters at Cornell University and the Massachusetts Institute of Technology (MIT) prompted popular and expert speculation of suicide contagion. However, some clustering is to be expected in any random process. Aim: This work tested whether suicide clusters at these two universities differed significantly from those expected under a homogeneous Poisson process, in which suicides occur randomly and independently of one another. Method: Suicide dates were collected for MIT and Cornell for 1990–2012. The Anderson-Darling statistic was used to test the goodness-of-fit of the intervals between suicides to distribution expected under the Poisson process. Results: Suicides at MIT were consistent with the homogeneous Poisson process, while those at Cornell showed clustering inconsistent with such a process (p = .05). Conclusions: The Anderson-Darling test provides a statistically powerful means to identify suicide clustering in small samples. Practitioners can use this method to test for clustering in relevant communities. The difference in clustering behavior between the two institutions suggests that more institutions should be studied to determine the prevalence of suicide clustering in universities and its causes.

scholarly journals Favorable Estimators for Fitting Pareto Models: A Study Using Goodness-of-fit Measures with Actual Data

2003 ◽  
Vol 33 (2) ◽  
pp. 365-381 ◽  
Author(s):  
Vytaras Brazauskas ◽  
Robert Serfling
Keyword(s):  
Goodness Of Fit ◽  
Real Data ◽  
Efficient Estimation ◽  
Small Samples ◽  
Robust Estimators ◽  
Trade Offs ◽  
Pareto Model ◽  
Kolmogorov Smirnov ◽  
Anderson Darling ◽  
Von Mises

Several recent papers treated robust and efficient estimation of tail index parameters for (equivalent) Pareto and truncated exponential models, for large and small samples. New robust estimators of “generalized median” (GM) and “trimmed mean” (T) type were introduced and shown to provide more favorable trade-offs between efficiency and robustness than several well-established estimators, including those corresponding to methods of maximum likelihood, quantiles, and percentile matching. Here we investigate performance of the above mentioned estimators on real data and establish — via the use of goodness-of-fit measures — that favorable theoretical properties of the GM and T type estimators translate into an excellent practical performance. Further, we arrive at guidelines for Pareto model diagnostics, testing, and selection of particular robust estimators in practice. Model fits provided by the estimators are ranked and compared on the basis of Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling statistics.

scholarly journals A Measure of Intra-University Collaboration: Faculty Gender Imbalance on Doctoral Dissertation Committees in Engineering Disciplines

10.28945/4141 ◽  
2018 ◽  
Vol 13 ◽  
pp. 457-469
Author(s):  
Emily M Welsh ◽  
Alexis R Abramson
Keyword(s):  
Gender Bias ◽  
Academic Career ◽  
Women Faculty ◽  
Future Research ◽  
Massachusetts Institute Of Technology ◽  
Gender Imbalance ◽  
Engineering Faculty ◽  
University Of Pittsburgh ◽  
Cornell University ◽  
Institute Of Technology

Aim/Purpose: This article presents an analysis of female faculty representation on dissertation committees in comparison to the percentage of women faculty in departments of engineering in 2013 and 2014. Background: Collaboration is an indication of a robust research program, and the consequences of collaboration may benefit one’s academic career in numerous ways. Gender bias, however, may impede the development of intra-university collaborations, thereby inhibiting professional success. Methodology: Nine universities were examined (Carnegie Mellon University, Case Western Reserve University, Cornell University, Duke University, Massachusetts Institute of Technology, Northwestern University, Rice University, University of Pittsburgh, and Vanderbilt University) across six different engineering departments (civil, chemical, mechanical, materials, biomedical, and electrical). Contribution: This paper reveals how an analysis of gender balance of faculty representation on doctoral committees can help advance an institution's understanding of the level to which collaboration with female colleagues may be occurring, thereby providing insight to the climate for women. Findings: A potential gender imbalance does exist in select cases. In aggregate, the percentage of female engineering faculty on dissertation committees compared to within each university revealed a disparity of less than 6% points. Recommendations for Practitioners: Examining how well represented female engineering faculty are on dissertation committees can be an important measure of levels of collaboration within an institution and of how well women are being integrated into the existing culture. Recommendation for Researchers: More in-depth research, including a study of correlation with other relevant indicators, may reveal additional insight to why gender bias exists on doctoral committees and how to lessen its occurrence. Impact on Society: The results of this study may increase awareness of gender bias and encourage faculty to be more inclusive and collaborative, particularly with their female colleagues, and as a result may help improve the climate for women faculty in engineering. Future Research: This study opens a discussion about the potential for gender imbalance and bias within an institution, particularly with respect to collaboration and inclusion. Future work may explore other indicators beyond doctoral committee representation.

scholarly journals Deep Learning-Based Arrhythmia Detection in Electrocardiograph

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yang Meng ◽  
Guoxin Liang ◽  
Mei Yue
Keyword(s):  
Heart Beat ◽  
Small Sample ◽  
Original Model ◽  
Massachusetts Institute Of Technology ◽  
Beth Israel Hospital ◽  
Arrhythmia Detection ◽  
Atrial Premature Beat ◽  
The Difference ◽  
Institute Of Technology ◽  
Sample Set

This study aimed to explore the application of electrocardiograph (ECG) in the diagnosis of arrhythmia based on the deep convolutional neural network (DCNN). ECG was classified and recognized with the DCNN. The specificity (Spe), sensitivity (Sen), accuracy (Acc), and area under curve (AUC) of the DCNN were evaluated in the Chinese Cardiovascular Disease Database (CCDD) and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, respectively. The results showed that in the CCDD, the original model tested by the small sample set had an accuracy (Acc) of 82.78% and AUC of 0.882, while the Acc and AUC of the translated model were 85.69% and 0.893, respectively, so the difference was notable ( P  < 0.05); the Acc of the original model and the translated model was 80.12% and 82.63%, respectively, in the large sample set, so the difference was obvious ( P  < 0.05). In the MIT-BIH database, the Acc of normal (N) heart beat (HB) (99.38%) was higher than that of the atrial premature beat (APB) (87.45%) ( P  < 0.05). In a word, applying the DCNN could improve the Acc of ECG for classification and recognition, so it could be well applied to ECG signal classification.

An Application of the Interferometer Strain Gage in Photoelasticity

1935 ◽  
Vol 2 (3) ◽  
pp. A99-A102
Author(s):  
R. W. Vose
Keyword(s):  
Strain Gage ◽  
Poisson’S Ratio ◽  
Poisson's Ratio ◽  
Massachusetts Institute ◽  
Massachusetts Institute Of Technology ◽  
Lateral Deformation ◽  
Principal Stresses ◽  
Photoelastic Analysis ◽  
The Difference ◽  
Institute Of Technology

Abstract This paper was written at the suggestion of Mr. Mieth Maeser, in response to numerous inquiries concerning the methods of photoelastic analysis in use at the Massachusetts Institute of Technology. By the use of any of the usual photoelastic methods the difference of the principal stresses and their direction at any point in a suitable loaded specimen are determined, and through a knowledge of Poisson’s ratio their sum is obtained (and a solution made possible) by a measurement of the lateral deformation of the specimen by means of an interferometer strain gage. This instrument, together with its accessories and their use, is illustrated and described in the paper. Examples of the problems solved by the use of the instrument show its accuracy and the consistency of the results obtained by the method.

scholarly journals Exploiting the information content of hydrological "outliers" for goodness-of-fit testing

2010 ◽  
Vol 7 (4) ◽  
pp. 4851-4874 ◽  
Author(s):  
F. Laio ◽  
P. Allamano ◽  
P. Claps
Keyword(s):  
Goodness Of Fit ◽  
Probabilistic Models ◽  
Small Samples ◽  
Goodness Of Fit Tests ◽  
Maximum Value ◽  
Parent Distribution ◽  
Fit Testing ◽  
Chi Squared ◽  
Anderson Darling ◽  
Hydrological Applications

Abstract. Validation of probabilistic models based on goodness-of-fit tests is an essential step for the frequency analysis of extreme events. The outcome of standard testing techniques, however, is mainly determined by the the behavior of the hypothetical model, FX(x), in the central part of the distribution, while the behavior in the tails of the distribution, which is indeed very relevant in hydrological applications, is relatively unimportant for the results of the tests. The maximum-value test, originally proposed as a technique for outlier detection, is a suitable, but seldom applied, technique that addresses this problem. The test is specifically targeted to verify if the maximum (or minimum) values in the sample are consistent with the hypothesis that the distribution FX(x) is the real parent distribution. The application of this test is hindered by the fact that the critical values for the test should be numerically obtained when the parameters of FX(x) are estimated on the same sample used for verification, which is the standard situation in hydrological applications. We propose here a simple, analytically explicit, technique to suitably account for this effect, based on the application of censored L-moments estimators of the parameters. We demonstrate, with an application that uses artificially generated samples, the superiority of this modified maximum-value test with respect to the standard version of the test. We also show that the test has comparable or larger power with respect to other goodness-of-fit tests (e.g., chi-squared test, Anderson-Darling test, Fung and Paul test), in particular when dealing with small samples (sample size lower than 20–25) and when the parent distribution is similar to the distribution being tested.

scholarly journals Favorable Estimators for Fitting Pareto Models: A Study Using Goodness-of-fit Measures with Actual Data

2003 ◽  
Vol 33 (02) ◽  
pp. 365-381 ◽  
Author(s):  
Vytaras Brazauskas ◽  
Robert Serfling
Keyword(s):  
Goodness Of Fit ◽  
Real Data ◽  
Efficient Estimation ◽  
Small Samples ◽  
Robust Estimators ◽  
Trade Offs ◽  
Pareto Model ◽  
Kolmogorov Smirnov ◽  
Anderson Darling ◽  
Von Mises

Several recent papers treated robust and efficient estimation of tail index parameters for (equivalent) Pareto and truncated exponential models, for large and small samples. New robust estimators of “generalized median” (GM) and “trimmed mean” (T) type were introduced and shown to provide more favorable trade-offs between efficiency and robustness than several well-established estimators, including those corresponding to methods of maximum likelihood, quantiles, and percentile matching. Here we investigate performance of the above mentioned estimators on real data and establish — via the use of goodness-of-fit measures — that favorable theoretical properties of the GM and T type estimators translate into an excellent practical performance. Further, we arrive at guidelines for Pareto model diagnostics, testing, and selection of particular robust estimators in practice. Model fits provided by the estimators are ranked and compared on the basis of Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling statistics.

Characterization Results Based on a Functional Derivative Approach

2001 ◽  
Vol 10 (5) ◽  
pp. 417-434 ◽  
Author(s):  
R. E. LILLO ◽  
M. MARTIN
Keyword(s):  
Poisson Process ◽  
Differential Calculus ◽  
Goodness Of Fit ◽  
Inverse Operator ◽  
Functional Derivative ◽  
Unified Approach ◽  
Homogeneous Poisson Process ◽  
Conditional Expectations ◽  
Different Types ◽  
Linear Differential

We introduce a notion of the derivative with respect to a function, not necessarily related to a probability distribution, which generalizes the concept of derivative as proposed by Lebesgue [14]. The differential calculus required to solve linear differential equations using this notion of the derivative is included in the paper. The definition given here may also be considered as the inverse operator of a modified notion of the Riemann–Stieltjes integral. Both this unified approach and the results of differential calculus allow us to characterize distributions in terms of three different types of conditional expectations. In applying these results, a test of goodness of fit is also indicated. Finally, two characterizations of a general Poisson process are included. Specifically, a useful result for the homogeneous Poisson process is generalized.

scholarly journals Exploiting the information content of hydrological ''outliers'' for goodness-of-fit testing

2010 ◽  
Vol 14 (10) ◽  
pp. 1909-1917 ◽  
Author(s):  
F. Laio ◽  
P. Allamano ◽  
P. Claps
Keyword(s):  
Goodness Of Fit ◽  
Probabilistic Models ◽  
Small Samples ◽  
Goodness Of Fit Tests ◽  
Maximum Value ◽  
Parent Distribution ◽  
Fit Testing ◽  
Chi Squared ◽  
Anderson Darling ◽  
Hydrological Applications

Abstract. Validation of probabilistic models based on goodness-of-fit tests is an essential step for the frequency analysis of extreme events. The outcome of standard testing techniques, however, is mainly determined by the behavior of the hypothetical model, FX(x), in the central part of the distribution, while the behavior in the tails of the distribution, which is indeed very relevant in hydrological applications, is relatively unimportant for the results of the tests. The maximum-value test, originally proposed as a technique for outlier detection, is a suitable, but seldom applied, technique that addresses this problem. The test is specifically targeted to verify if the maximum (or minimum) values in the sample are consistent with the hypothesis that the distribution FX(x) is the real parent distribution. The application of this test is hindered by the fact that the critical values for the test should be numerically obtained when the parameters of FX(x) are estimated on the same sample used for verification, which is the standard situation in hydrological applications. We propose here a simple, analytically explicit, technique to suitably account for this effect, based on the application of censored L-moments estimators of the parameters. We demonstrate, with an application that uses artificially generated samples, the superiority of this modified maximum-value test with respect to the standard version of the test. We also show that the test has comparable or larger power with respect to other goodness-of-fit tests (e.g., chi-squared test, Anderson-Darling test, Fung and Paul test), in particular when dealing with small samples (sample size lower than 20–25) and when the parent distribution is similar to the distribution being tested.

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