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Basic Approaches of Artificial Intelligence and Machine Learning in Thermal Image Processing
Published in U. Snekhalatha, K. Palani Thanaraj, Kurt Ammer, Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications, 2023
U. Snekhalatha, K. Palani Thanaraj, Kurt Ammer
The GLCM is based on a statistical method that is used for analyzing the texture feature which gives information about the spatial relationship of pixels. In an image, both the column and row will be identical to the number of gray levels denoted as “g” in the GLCM matrix. The texture of the image is used to calculate the pixel with particular values in a specific spatial relationship. Data are divided into first, second, and higher-order statistics. In that way, the second order is a statistical method in the gray level co-occurrence matrix.
Trial Design for Precision Medicine
Published in Mark Chang, John Balser, Jim Roach, Robin Bliss, Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials, 2019
Mark Chang, John Balser, Jim Roach, Robin Bliss
The FWER control methods are based on the joint distribution of the order statistics (the test statistics). You may opt to skip this section with impairing you to design basket design since you can use trial simulation approach presented later.
Nonparametric Comparisons of Distributions
Published in Albert Vexler, Alan D. Hutson, Xiwei Chen, Statistical Testing Strategies in the Health Sciences, 2017
Albert Vexler, Alan D. Hutson, Xiwei Chen
To outline the K-sample procedure in the empirical likelihood framework, we suppose that the data consist of K independent samples and we are interested in testing whether all K-samples are distributed identically in a nonparametric fashion. Assume that the K-samples are represented by the vectors of observations given as from the corresponding distribution functions . We now want to test the hypothesis H0: versus H1: not all . If the corresponding density functions are known, the LR statistic has the form , where denotes the density function of the jth sample under H1, fZ is the theoretical density function of observations under H0. Let , be the order statistics per sample based on the observations . As before, we denote and . We apply the maximum EL method to obtain the density-based empirical likelihood (DBEL) ratio test statistic
A new alternative quantile regression model for the bounded response with educational measurements applications of OECD countries
Published in Journal of Applied Statistics, 2023
Mustafa Ç. Korkmaz, Christophe Chesneau, Zehra Sedef Korkmaz
The notion of order statistics is one of most useful in probability and statistics, from both the theoretical and practical sides. This motivates the discussion of crucial features of the order statistics of the 11] are used. Let us consider n rvs denoted by 1) and (2),
Distribution-free monitoring schemes based on order statistics: a general approach
Published in Journal of Applied Statistics, 2020
Ioannis S. Triantafyllou, Nikolaos I. Panayiotou
The family of nonparametric monitoring schemes introduced in the present manuscript includes as special cases several distribution-free control charts, which have been already introduced in the literature. For example, the monitoring scheme established by Balakrishnan et al. [3] calls for two specific order statistics from the reference sample of size m, say 3] are described as follows a quantile the number of observations from the test sample that lie between the control limits.
Generalized inverted Kumaraswamy generated family of distributions: theory and applications
Published in Journal of Applied Statistics, 2019
Farrukh Jamal, Muhammad Arslan Nasir, Gamze Ozel, M. Elgarhy, Naushad Mamode Khan
Order statistics have been extensively applied in many fields of statistics, such as reliability and life testing. They enter in the problems of estimation and hypothesis tests in a variety of ways. We now discuss some properties of the order statistics for the GIKw-G family. Let ith-order statistic from a random sample ith-order statistic is given by 5) in Equation (8), we have 6) and (7), we obtain n (for 14]. n is a natural number. Then, we have 9) becomes ith-order statistic is given by