APPENDIX 8: TWO-TAILED CRITICAL VALUES OF THE LARGEST DIFFERENCE IN THE KOLMOGOROV-SMIRNOV ONE-SAMPLE TEST APPENDIX 9: ONE-TAILED 0.05 CRITICAL VALUES OF THE KOLMOGOROV-SMIRNOV TEST FOR SMALL SAMPLES

2002 ◽  
pp. 294-294
Keyword(s):  
Critical Values ◽  
Small Samples ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
Smirnov Test

scholarly journals Comparison of methods of submission of suspected adverse drug reactions to the ADR monitoring centers in India

2017 ◽  
Vol 6 (8) ◽  
pp. 2032
Author(s):  
Sachin Kumar Kuchya ◽  
Sarita Shrivastav
Keyword(s):  
Comparison Of Methods ◽  
Drug Reactions ◽  
Monitoring Centre ◽  
Adr Reporting ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
The Difference ◽  
Independent Assessment ◽  
Suspected Adverse Drug Reactions ◽  
Smirnov Test

Background: Traditionally, paper based suspected ADR forms were the only way of submitting suspected ADR (sADR) data. Recently the mobile android based ADR reporting app© (App©) has also been developed and a copyright was granted to the author. This study is done to assess the two, viz. paper based and App based, methods of submission of sADR data.Methods: The sADR data submitted to the ADR Monitoring Centre (AMC), at Department of Pharmacology, NSCB MC Jabalpur. There is no such scale to assess the completeness of suspected ADR data received by individual AMCs. Therefore, appropriate algorithm and scale for Completeness scoring of filled sADR forms was designed, the basic tenets were adhered. A set of 10 sADR forms, submitted by either method, were subjected to Independent assessment by 3 assessors, who were not part of this study. The scores were then subjected to analysis, which revealed minimal variation across the assessment. Hence, the scale was adopted for the study.Results: A total of 403 sADR’s submitted to our AMC, were screened and subjected to scoring for completeness. Upon screening, 96.2% (257/267) sADR submitted via paper based sADR form and 100% (136/136) of those submitted via App stood valid, and hence included in the study. All the suspected ADR (sADR) submitted via ADR Reporting app were, complete. The sADR data submitted via ADR reporting app, had an average completeness score of 34.7±2.4 while those submitted via paper based form had an average of 29.2±2.4. The difference is highly significant on Wilcoxon two sample test (p<0.001) and Kolmogorov-Smirnov test (p<0.001).Conclusions: Compared to traditional paper based system, the ADR reporting app based sADR submission, is a better method.

scholarly journals A sketch for the KS test for Big Data

2021 ◽  
Author(s):  
Thalis D. Galeno ◽  
João Gama ◽  
Douglas O. Cardoso
Keyword(s):  
Big Data ◽  
Goodness Of Fit ◽  
Absolute Error ◽  
Test Statistic ◽  
Input Stream ◽  
Reference Distribution ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
The One ◽  
Smirnov Test

Motivated by the challenges of Big Data, this paper presents an approximative algorithm to assess the Kolmogorov-Smirnov test. This goodness of fit statistical test is extensively used because it is non-parametric. This work focuses on the one-sample test, which considers the hypothesis that a given univariate sample follows some reference distribution. The method allows to evaluate the departure from such a distribution of a input stream, being space and time efficient. We show the accuracy of our algorithm by making several experiments in different scenarios: varying reference distribution and its parameters, sample size, and available memory. The performance of rival methods, some of which are considered the state-of-the-art, were compared. It is demonstrated that our algorithm is superior in most of the cases, considering the absolute error of the test statistic.

scholarly journals A Hypothesis Test for the Goodness-of-Fit of the Marginal Distribution of a Time Series with Application to Stablecoin Data

2021 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Mark Levene
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Marginal Distribution ◽  
Hypothesis Test ◽  
Data Sets ◽  
Test Statistic ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
Heavy Tailed ◽  
Jensen Shannon Divergence

A bootstrap-based hypothesis test of the goodness-of-fit for the marginal distribution of a time series is presented. Two metrics, the empirical survival Jensen–Shannon divergence (ESJS) and the Kolmogorov–Smirnov two-sample test statistic (KS2), are compared on four data sets—three stablecoin time series and a Bitcoin time series. We demonstrate that, after applying first-order differencing, all the data sets fit heavy-tailed α-stable distributions with 1<α<2 at the 95% confidence level. Moreover, ESJS is more powerful than KS2 on these data sets, since the widths of the derived confidence intervals for KS2 are, proportionately, much larger than those of ESJS.

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