{"id":1167,"date":"2015-10-17T12:49:34","date_gmt":"2015-10-17T09:49:34","guid":{"rendered":"http:\/\/insanveinsan.org\/?p=1167"},"modified":"2018-04-09T23:03:24","modified_gmt":"2018-04-09T20:03:24","slug":"bagimsiz-degiskenin-pareto-dagilimina-sahip-olmasi-durumunda-uyelik-fonksiyonunun-dayali-parametre-tahmini","status":"publish","type":"post","link":"https:\/\/www.insanveinsan.org\/en\/bagimsiz-degiskenin-pareto-dagilimina-sahip-olmasi-durumunda-uyelik-fonksiyonunun-dayali-parametre-tahmini\/","title":{"rendered":"Parameter Estimation Based on Membership Function When an Independent Variable Has Pareto Distribution"},"content":{"rendered":"

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\u00d6zet: <\/strong> If classical methods are being employed in simple and multiple linear regression analysis, estimation of parameters related to the model is based on some assumptions. If the data have any distribution other than normal distribution in estimation of unknown parameters of linear regression models, the analysis has to go beyond classical analysis. In such cases analysis methods based on fuzzy logic manifest themselves as alternative ways. In this paper, an algorithm has been proposed in order to be able to estimate the unknown parameters of multiple linear regression model in the case that any independent variable has a Pareto distribution and incompatible observations exist in data set and estimations obtained from this algorithm have been compared with estimations obtained from the methods existing in the literature.<\/p>\n

Keywords: <\/strong> Parameter Estimation, Fuzzy Membership Function, Pareto Distribution.<\/p>\n

Ba\u011f\u0131ms\u0131z De\u011fi\u015fkenin Pareto Da\u011f\u0131l\u0131m\u0131na Sahip Olmas\u0131 Durumunda \u00dcyelik Fonksiyonunun Dayal\u0131 Parametre Tahmini<\/h1>\n

Abstract: <\/strong> Basit ve \u00e7oklu do\u011frusal regresyon \u00e7\u00f6z\u00fcmlemesinde klasik metotlardan yararlan\u0131l\u0131yorsa, modele ili\u015fkin parametrelerin tahmini baz\u0131 varsay\u0131mlara dayan\u0131r. Do\u011frusal regresyon modellerinin bilinmeyen parametrelerinin tahmininde verilerin normal da\u011f\u0131l\u0131m d\u0131\u015f\u0131nda bir da\u011f\u0131l\u0131ma sahip olmas\u0131 durumu tahmin s\u00fcrecinde klasik \u00e7\u00f6z\u00fcmlemelerin d\u0131\u015f\u0131na \u00e7\u0131k\u0131lmas\u0131n\u0131 gerektirir. B\u00f6yle durumlarda bulan\u0131k mant\u0131\u011fa dayal\u0131 \u00e7\u00f6z\u00fcmleme y\u00f6ntemleri alternatif y\u00f6ntemler olarak kendini g\u00f6stermektedir. Bu \u00e7al\u0131\u015fmada, ba\u011f\u0131ms\u0131z de\u011fi\u015fkenlerden herhangi birinin Pareto da\u011f\u0131l\u0131m\u0131na sahip olmas\u0131 ve veri setinde ayk\u0131r\u0131 g\u00f6zlemlerin mevcut olmas\u0131 durumunda, kurulacak \u00e7oklu do\u011frusal regresyon modelinin bilinmeyen parametrelerini tahmin etmek i\u00e7in bir algoritma \u00f6nerilerek, algoritmadan elde edilen tahminler literat\u00fcrde yer alan mevcut y\u00f6ntemlerden elde edilen tahminler ile kar\u015f\u0131la\u015ft\u0131r\u0131lm\u0131\u015ft\u0131r.<\/p>\n

Anahtar kelimeler: <\/strong> Parametre Tahmini, Bulan\u0131k \u00dcyelik Fonksiyonu, Pareto Da\u011f\u0131l\u0131m\u0131.<\/p>\n

<\/p>","protected":false},"excerpt":{"rendered":"

T\u00fcrkan ERBAY DALKILI\u00c7
\nTu\u011fba KARAN<\/p>\n

\n

DOI: 10.29224\/insanveinsan.280006<\/a><\/p>\n

\n

Issue 6, Fall 2015<\/a><\/p>\n

\n

\"\"
\nTam metin \/ Full text
\n(Turkish)<\/p>\n

\"Creative<\/a>
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License<\/a>.<\/p>\n

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