Determining the Minimum Sample Size of Audit Data Required to Profile User Behavior and Detect Anomaly Intrusion

2006 ◽  
Vol 2 (3) ◽  
pp. 31-45 ◽  
Author(s):  
Yun Wang ◽  
Sharon-Lise T. Normand
Keyword(s):  
Sample Size ◽  
User Behavior ◽  
Minimum Sample Size ◽  
Audit Data ◽  
Minimum Sample

scholarly journals Minimum sample size for external validation of a clinical prediction model with a binary outcome

2021 ◽  
Author(s):  
Richard D. Riley ◽  
Thomas P. A. Debray ◽  
Gary S. Collins ◽  
Lucinda Archer ◽  
Joie Ensor ◽  
...  
Keyword(s):  
Prediction Model ◽  
Sample Size ◽  
External Validation ◽  
Minimum Sample Size ◽  
Binary Outcome ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Minimum Sample

Criterion to determine the minimum sample size for load spectrum measurement and statistical extrapolation

Measurement ◽  
2021 ◽  
Vol 178 ◽  
pp. 109387
Author(s):  
Guofeng Zheng ◽  
Qiongyao Wang ◽  
Chuan Cai
Keyword(s):  
Sample Size ◽  
Minimum Sample Size ◽  
Load Spectrum ◽  
Spectrum Measurement ◽  
Minimum Sample

Determination of the minimum sample size for the transmission load of a wheel loader based on multi-criteria decision-making technology

2012 ◽  
Vol 49 (3-4) ◽  
pp. 147-160 ◽  
Author(s):  
Jixin Wang ◽  
Naixiang Wang ◽  
Zhenyu Wang ◽  
Yingshuang Zhang ◽  
Lin Liu
Keyword(s):  
Decision Making ◽  
Sample Size ◽  
Multi Criteria Decision Making ◽  
Minimum Sample Size ◽  
Wheel Loader ◽  
Minimum Sample

Minimum Sample Size of Kidney Biopsies for Semiquantitative and Quantitative Evaluation

Nephron ◽  
1983 ◽  
Vol 34 (3) ◽  
pp. 192-195 ◽  
Author(s):  
M. Oberholzer ◽  
J. Torhorst ◽  
E. Perret ◽  
M.J. Mihatsch
Keyword(s):  
Sample Size ◽  
Quantitative Evaluation ◽  
Minimum Sample Size ◽  
Minimum Sample

scholarly journals Methods and Rule-of-Thumbs in The Determination of Minimum Sample Size When Appling Structural Equation Modelling: A Review

2020 ◽  
Vol 15 ◽  
pp. 102-107
Author(s):  
Hunuwala Malawarage Suranjan Priyanath ◽  
Ranatunga RVSPK ◽  
Megama RGN
Keyword(s):  
Structural Equation Modeling ◽  
Sample Size ◽  
Structural Equation ◽  
Sample Size Determination ◽  
Equation Modeling ◽  
Minimum Sample Size ◽  
Research Project ◽  
Rules Of Thumb ◽  
Minimum Sample

Basic methods and techniques involved in the determination of minimum sample size at the use of Structural Equation Modeling (SEM) in a research project, is one of the crucial problems faced by researchers since there were some controversy among scholars regarding methods and rule-of-thumbs involved in the determination of minimum sample size when applying Structural Equation Modeling (SEM). Therefore, this paper attempts to make a review of the methods and rule-of-thumbs involved in the determination of sample size at the use of SEM in order to identify more suitable methods. The paper collected research articles related to the sample size determination for SEM and review the methods and rules-of-thumb employed by different scholars. The study found that a large number of methods and rules-of-thumb have been employed by different scholars. The paper evaluated the surface mechanism and rules-of-thumb of more than twelve previous methods that contained their own advantages and limitations. Finally, the study identified two methods that are more suitable in methodologically and technically which have identified by non-robust scholars who deeply addressed all the aspects of the techniques in the determination of minimum sample size for SEM analysis and thus, the prepare recommends these two methods to rectify the issue of the determination of minimum sample size when using SEM in a research project.

Minimum Sample Size and Optimal Positioning of Flanking Markers in Marker-Assisted Backcrossing for Transfer of a Target Gene

Crop Science ◽  
1999 ◽  
Vol 39 (6) ◽  
pp. 1903-1903
Author(s):  
Matthias Frisch ◽  
Martin Bohn ◽  
Albrecht E. Melchinger
Keyword(s):  
Sample Size ◽  
Target Gene ◽  
Minimum Sample Size ◽  
Minimum Sample ◽  
Marker Assisted Backcrossing

Minimum Sampling Size of Floating Cars for Urban Link Travel Time Distribution Estimation

2019 ◽  
Vol 2673 (3) ◽  
pp. 24-43 ◽  
Author(s):  
Meiping Yun ◽  
Wenwen Qin
Keyword(s):  
Sample Size ◽  
Travel Time ◽  
Real Time ◽  
Time Distribution ◽  
Ground Truth ◽  
Sample Size Determination ◽  
Minimum Sample Size ◽  
Travel Time Distribution ◽  
Minimum Sample ◽  
Link Travel Time

Despite the wide application of floating car data (FCD) in urban link travel time estimation, limited efforts have been made to determine the minimum sample size of floating cars appropriate to the requirements for travel time distribution (TTD) estimation. This study develops a framework for seeking the required minimum number of travel time observations generated from FCD for urban link TTD estimation. The basic idea is to test how, with a decreasing the number of observations, the similarities between the distribution of estimated travel time from observations and those from the ground-truth vary. These are measured by employing the Hellinger Distance (HD) and Kolmogorov-Smirnov (KS) tests. Finally, the minimum sample size is determined by the HD value, ensuring that corresponding distribution passes the KS test. The proposed method is validated with the sources of FCD and Radio Frequency Identification Data (RFID) collected from an urban arterial in Nanjing, China. The results indicate that: (1) the average travel times derived from FCD give good estimation accuracy for real-time application; (2) the minimum required sample size range changes with the extent of time-varying fluctuations in traffic flows; (3) the minimum sample size determination is sensitive to whether observations are aggregated near each peak in the multistate distribution; (4) sparse and incomplete observations from FCD in most time periods cannot be used to achieve the minimum sample size. Moreover, this would produce a significant deviation from the ground-truth distributions. Finally, FCD is strongly recommended for better TTD estimation incorporating both historical trends and real-time observations.

scholarly journals Precision and Sample Size Requirements for Regression-Based Norming Methods for Change Scores

Assessment ◽  
2020 ◽  
pp. 107319112091360
Author(s):  
Zhengguo Gu ◽  
Wilco H. M. Emons ◽  
Klaas Sijtsma
Keyword(s):  
Sample Size ◽  
Simulation Study ◽  
Change Score ◽  
Minimum Sample Size ◽  
Change Scores ◽  
Satisfactory Precision ◽  
Similarities And Differences ◽  
Minimum Sample

To interpret a person’s change score, one typically transforms the change score into, for example, a percentile, so that one knows a person’s location in a distribution of change scores. Transformed scores are referred to as norms and the construction of norms is referred to as norming. Two often-used norming methods for change scores are the regression-based change approach and the T Scores for Change method. In this article, we discuss the similarities and differences between these norming methods, and use a simulation study to systematically examine the precision of the two methods and to establish the minimum sample size requirements for satisfactory precision.

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