Çoklu Doğrusallık ve Değişen Varyans Altında Farklı Ridge Parametrelerinin Bir Karşılaştırması

Year 2019, , 381 - 389, 25.08.2019

Volkan Sevinç , Atila Göktaş

https://doi.org/10.19113/sdufenbed.484275

Cited By: 4

Abstract

Uygun bir
doğrusal regresyon modeli tahmin edilmesi sırasında karşılaşılan ana
problemlerden biri bağımsız değişkenler yüksek korelasyona sahip olduğu zaman
ortaya çıkan çoklu doğrusallıktır. Bu sorunun giderilmesi için sıradan en küçük
karelere bir alternatif yöntem olarak tanıtılan ridge regresyon tahmincisi
kullanılmaktadır. Sabit varyanslar varsayımını bozan değişen varyans durumu,
regresyon tahmininde diğer ana sorunlardan biridir. Daha sağlam bir doğrusal
regresyon eşitliği tahmin edebilmek için bu bozulma sorununa çözüm olarak
ağırlıklı en küçük kareler tahmini kullanılır. Ancak, hem çoklu doğrusallık hem
de değişen varyans sorunu mevcut olduğunda, ağırlıklı ridge regresyon tahminine
başvurulmalıdır. Ridge regresyon, kesin bir hesaplama formülü bulunmayan ridge
parametresine bağlıdır. Literatürde önerilen bir çok ridge parametresi
bulunmaktadır. Hem çoklu doğrusallık hem de değişen varyans içeren veri için bu
ridge parametrelerinin performanslarını analiz etmeye yönelik bir simülasyon
çalışması düzenlenmiştir. Farklı örnek hacimleri, farklı bağımsız değişken
sayıları ve farklı çoklu doğrusallık dereceleri kullanılmıştır. Ridge
parametrelerinin performansları ortalama hata kareleri değerleri göz önüne
alınarak karşılaştırılmıştır. Çalışma aynı zamanda, verinin hem çoklu
doğrusallık hem de değişen varyansa sahip olduğu durumda, ridge
parametrelerinin performanslarının, verinin sadece çoklu doğrusallığa sahip
olduğu durumdakinden farklı olduğunu göstermiştir.

Keywords

Çoklu doğrusallık, Ridge parametresi, Değişen varyans, Ridge regresyon, Ağırlıklı ridge regresyon

A Comparison of Different Ridge Parameters under Both Multicollinearity and Heteroscedasticity

Abstract

One of
the major problems in fitting an appropriate linear regression model is multicollinearity
which occurs when regressors are highly correlated. To overcome this problem, ridge
regression estimator which is an alternative method to the ordinary least
squares (OLS) estimator, has been used. Heteroscedasticity, which violates the
assumption of constant variances, is another major problem in regression
estimation. To solve this violation problem, weighted least squares estimation
is used to fit a more robust linear regression equation. However, when there is
both multicollinearity and heteroscedasticity problem, weighted ridge
regression estimation should be employed. Ridge regression depends on the ridge
parameter which does not have an explicit form of calculation. There are
various ridge parameters proposed in the literature. A simulation study was conducted to compare the
performances of these ridge parameters for both multicollinear and
heteroscedastic data. The following factors were varied: the number of
regressors, sample sizes and degrees of multicollinearity. The performances of the parameters were compared
using mean square error. The
study also shows that when the data are both heteroscedastic and multicollinear,
the estimation performances of the ridge parameters differs from the case for
only multicollinear data.

Keywords

Multicollinearity, Ridge parameter, Heteroscedasticity, Ridge regression, Weighted ridge regression

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