An Exhaustive Power Comparison of Normality Tests

Arnastauskaitė, Jurgita; Ruzgas, Tomas; Bražėnas, Mindaugas

doi:10.3390/math9070788

Cited by 37 publications

(52 citation statements)

References 27 publications

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“…Some of the advantages of Lilliefors test includes the ability to perform the normality test without specifying distribution parameters (i.e. mean and standard deviation), the availability of the test package in the data processor, and the availability of critical values for small sample sizes; the Lilliefors test also showed similar statistical power as χ 2 test for small sample sizes such as the case of the current flight tests [60]. These statistics are further compared to UAS distribution predictions simulated using models from Ref.…”

Section: Track Data Analysis and Discussionmentioning

confidence: 99%

Investigation and Modeling of Flight Technical Error (FTE) Associated With UAS Operating With and Without Pilot Guidance

Wang

Low

2021

IEEE Trans. Veh. Technol.

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With the increasing interest in the utilization of urban airspace for unmanned aerial system (UAS) operations, be it for cargo delivery or passenger transportation, the future city sky could become quite a crowded place if all those visions become reality. In such case, the question of how close two operations could be conducted while sharing an airspace must be answered to ensure the safe and efficient use of the urban airspace. The lateral separation needed to prevent inadvertent intrusion by neighboring tracks is especially important when designing airspace corridors constrained by the urban landscape. This paper documents a series of flight tests conducted in open fields with the goal of assessing the along-track (longitudinal), cross-track (lateral),and altitude (height) deviation under two different flight conditions: operator guided operation within Visual Line of Sight (VLoS), and the waypoint-guided mission analogous to operating Beyond Visual Line of Sight (BVLoS). The flight test statistics were also compared to the Monte-Carlo based path prediction model that was used in earlier studies for collision prediction. The goal is to determine the Flight Technical Error (FTE) that forms a part of the Total System Error (TSE) for Performance Based Navigation (PBN) of UAS to support the establishment of separation requirements in urban airspace.

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Section: Track Data Analysis and Discussionmentioning

confidence: 99%

Investigation and Modeling of Flight Technical Error (FTE) Associated With UAS Operating With and Without Pilot Guidance

Wang

Low

2021

IEEE Trans. Veh. Technol.

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“…Suppose x i is the i-th digital image pixel value for the predictor variable X i , F(x i ) is the cumulative distribution function, F(z i ) is the standard cumulative normal distribution function Z i and n is the sample size. Kolmogorov-Smirnov (KS), Cramer von Mises (CvM), and Anderson-Darling tests statistics are shown by (Ade soye et al, 2016;Arnastauskait ė et al, 2021;Razali et al, 2011;Jäntschi and Bolboacă, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Jarque-Bera test is based on the sample skewness and sample kurtosis, which uses the Lagrange multiplier procedure on the Pearson family of distributions to obtain tests for normality(Ade soye et al, 2016) . The Shapiro-Wilk test can detect deviations from the Gaussian distribution due to skewness, kurtosis, or both(Ade soye et al, 2016;Arnastauskait ė et al, 2021;Razali et al, 2011).…”

mentioning

confidence: 99%

Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor

Resti

Irsan

Putri

et al. 2022

Sci.Technol.Indonesia

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Statistical machine learning has developed into integral components of contemporary scientific methodology. This integration provides automated procedures for predicting phenomena, case diagnosis, or object identification based on previous observations, uncovering patterns underlying data, and providing insights into the problem. Identification of corn plant diseases and pests using it has become popular recently. Corn (Zea mays L) is one of the essential carbohydrate-producing foodstuffs besides wheat and rice. Corn plants are sensitive to pests and diseases, resulting in a decrease in the quantity and quality of the production. Eradicate pests and diseases according to their type is a solution to overcome the problem of disease in corn plants. This research aims to identify corn plant diseases and pests based on the digital image using the Multinomial Naïve Bayes and K-Nearest Neighbor methods. The data used consisted of 761 digital images with six classes of corn plants disease and pest. The investigation shows that the K-Nearest Neighbor method has a better predictive performance than the Multinomial Naïve Bayes (MNB) method. The MNB method with two categories has an accuracy level of 92.72%, a precision level of 79.88%, a recall level of 79.24%, F1-score 78.17%, kappa 72.44%, and AUC 71.91%. Simultaneously, the K-Nearest Neighbor approach with k=3 has an accuracy of 99.54 %, a precision of 88.57%, recall 94.38%, F1-score 93.59%, kappa 94.30%, and AUC 95.45%.

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“…There are many useful tips for creating discrete random variables from continuous ones: through discretization, data can actually be summarized and simplified; in addition, they can also become easier to understand, use, and explain for researchers (see [12]). Other tests appearing in the literature are suitable for both discrete and continuous distributions (see, for example, [13,14]).…”

Section: Introductionmentioning

confidence: 99%

Generating Optimal Discrete Analogue of the Generalized Pareto Distribution under Bayesian Inference with Applications

Ahmad

Almetwally

2022

Symmetry

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This paper studies three discretization methods to formulate discrete analogues of the well-known continuous generalized Pareto distribution. The generalized Pareto distribution provides a wide variety of probability spaces, which support threshold exceedances, and hence, it is suitable for modeling many failure time issues. Bayesian inference is applied to estimate the discrete models with different symmetric and asymmetric loss functions. The symmetric loss function being used is the squared error loss function, while the two asymmetric loss functions are the linear exponential and general entropy loss functions. A detailed simulation analysis was performed to compare the performance of the Bayesian estimation using the proposed loss functions. In addition, the applicability of the optimal discrete generalized Pareto distribution was compared with other discrete distributions. The comparison was based on different goodness-of-fit criteria. The results of the study reveal that the discretized generalized Pareto distribution is quite an attractive alternative to other discrete competitive distributions.

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An Exhaustive Power Comparison of Normality Tests

Cited by 37 publications

References 27 publications

Investigation and Modeling of Flight Technical Error (FTE) Associated With UAS Operating With and Without Pilot Guidance

Investigation and Modeling of Flight Technical Error (FTE) Associated With UAS Operating With and Without Pilot Guidance

Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor

Generating Optimal Discrete Analogue of the Generalized Pareto Distribution under Bayesian Inference with Applications

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