{
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    "title": "A Simulation Study on Dependency Structure of Order Statistics",
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    "abstract": "In this study, dependency structure of order statistics which are widely used in statistical hypothesis, prediction problems, statistical process controls, reliability, risk management and many applied areas have been investigated with copulas. Dependency relations of the extreme order statistics X X (1) ( ) , n have been examined for different n values and the suitability of Clayton, Gumbel, Gaussian and min-max-copula families to the data set has been explored by Chi-square goodness of fit test from depending on the change of these relations. Let’s assume that random samples are drawn from X ~ N(0,1) distribution and X X X (1) (2) ( ) , ,..., n are order statistics of these samples. For different n values ( n =2, 0, 20,100) the results classified with 4 × 4 quartile points for  X1,Xn extreme order statistics are obtained. Chi-square ( 2 ) values for each copula family is calculated by using the obtained values and repeated r  500times. According to the analysis results, min-max copula has been found the best-fit copula for small n values. However, none of the copulas have been found suitable for large n values. Consequently, although the-best fit copula for dependency structure of order statistics is min-max copula, it is shown with simulation that min-max copula is more convenient for small n values.  ",
    "language": "en",
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    "resource_url": "https://www.set-science.com/a/863f475f-3eb3-46bc-8894-e56827fd4c66",
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        {
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            "given": "Ferhan",
            "family": "Baş Kaman",
            "full": "Ferhan Baş Kaman",
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            "email": "basferhan@gmail.com",
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            "affiliations": [
                {
                    "name": "Department of Banking and Finance, Graduate School of Social Sciences, Yıldırım Beyazıt University ",
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        {
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            "given": "Hülya",
            "family": "Olmuş",
            "full": "Hülya Olmuş",
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            "email": "hulya@gazi.edu.tr",
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            "sequence": "additional",
            "affiliations": [
                {
                    "name": "Department of Statistics, Graduate School of Natural and Applied Sciences, Gazi University  ",
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    "keywords": [
        "dependent structure, order statistics, copula"
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    "translations": [
        {
            "language": "en",
            "title": "A Simulation Study on Dependency Structure of Order Statistics",
            "abstract": "In this study, dependency structure of order statistics which are widely used in statistical hypothesis, prediction problems, statistical process controls, reliability, risk management and many applied areas have been investigated with copulas. Dependency relations of the extreme order statistics X X (1) ( ) , n have been examined for different n values and the suitability of Clayton, Gumbel, Gaussian and min-max-copula families to the data set has been explored by Chi-square goodness of fit test from depending on the change of these relations. Let’s assume that random samples are drawn from X ~ N(0,1) distribution and X X X (1) (2) ( ) , ,..., n are order statistics of these samples. For different n values ( n =2, 0, 20,100) the results classified with 4 × 4 quartile points for  X1,Xn extreme order statistics are obtained. Chi-square ( 2 ) values for each copula family is calculated by using the obtained values and repeated r  500times. According to the analysis results, min-max copula has been found the best-fit copula for small n values. However, none of the copulas have been found suitable for large n values. Consequently, although the-best fit copula for dependency structure of order statistics is min-max copula, it is shown with simulation that min-max copula is more convenient for small n values.  ",
            "keywords": [
                "dependent structure, order statistics, copula"
            ]
        }
    ],
    "subjects": [],
    "subject_names": [],
    "published_date": "2018-06-23",
    "published_year": "2018",
    "received_date": null,
    "accepted_date": null,
    "volume": "2",
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    "page_first": "136",
    "page_last": "136",
    "article_number": null,
    "conference": {
        "short_name": "ISAS 2018",
        "full_title": "Ist International Symposium on Innovative Approaches in Scientific Studies",
        "name": "Ist International Symposium on Innovative Approaches in Scientific Studies",
        "acronym": "ISAS 2018",
        "location": "Kemer-Antalya, Türkiye",
        "start_date": "2018-04-11",
        "end_date": null,
        "public_id": "1d94fec9-c4fe-4034-9157-0b205eef4e56"
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    "proceedings": {
        "title": "Ist International Symposium on Innovative Approaches in Scientific Studies",
        "series_title": "Setsci conference indexing system",
        "publisher": "SET Technology",
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        "issn": "2618-6446",
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        "slug": "setsci-volume-2-2018-legacy-5",
        "public_id": "03da7b1c-cb6e-4f3b-aee4-ac41ee60fd75",
        "url": "https://www.set-science.com/p/setsci-volume-2-2018-legacy-5"
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        "url": "https://creativecommons.org/licenses/by/4.0/",
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    "copyright": {
        "holder": "SET Technology",
        "year": "2018"
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    "references": [
        {
            "order": 1,
            "raw": "Arnold, B.C., Balakrishnan, N. and Nagaraja, H.N. (1992). A First Course in Order Statistics. New York: John\r\nWiley&Sons, 9-21.\r\nEmbrechts, P., F. Lindskog, and A. McNeil. (2003). Modeling dependence with copulas and applications to risk\r\nmanagement. In S.T. Rachev, editor, Handbook of Heavy Tailed Distribution in Finance, chapter 8, JAI Press:\r\nHandbooks in Finance, Vol. 1. pp. 357-360.\r\nGenest, C. and Rivest, L. P. (1993). Statistical inference procedures for bivariate Archimedean copulas. Journal\r\nof the American Statistical Association, 88 (3): 1034-1043.\r\nNelsen, R. B. (2006). An Introduction to Copulas.(Second edition), New York:Springer, 7-211.\r\nSklar, A. (1959). Fonctions de repartitiona n dimesions et leursmarges. Publ. Inst. Statist. Univ., Paris 8: 229-\r\n231.",
            "doi": null,
            "title": null,
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        }
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    "history": {
        "published": "2018-06-23"
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