Estimating of Birth Weight Using Placental Characteristics in The Presence of Multicollinearity
Öz
Anahtar Kelimeler
Kaynakça
- Albayrak AS. 2005. An alternative bias estimation technique and an application of the least-squares technique in multiple linear connections. Zonguldak Karaelmas Univ J Soc Sci, 1: 105-126.
- Alkass JE, Merkhan KY, Hamo RAH. 2013. Placental traits and their relation with birth weight in Meriz and Black goats. Sci J Anim Sci, 2: 168–172.
- Alpu O, Samkar H. 2010. Liu estimator based on an m estimator. Turkiye Klinikleri J Biostat, 2 (2): 49-53.
- Ari A, Onder H. 2013. Regression models used for different data structures. Anadolu J Agr Sci, 28 (3): 168-174.
- Brzozowska A, Wojtasiak N, Błaszczyk B, Stankiewicz T, Wieczorek-Dąbrowska M, Udała J. 2020. The effects of non-genetic factors on themorphometric parameters of sheep placenta and the birth weight of lambs. Large Anim Rev, 26: 119-126.
- Cankaya S, Eker S, Abaci SH. 2019. Comparison of Least Squares, Ridge Regression and Principal Component Approaches in the Presence of Multicollinearity in Regression Analysis. Turkish J Agriculture-Food Sci and Tech, 7(8): 1166-1172. DOI: 10.24925/turjaf.v7i8.1166-1172.2515.
- Dwyer CM, Calvert SK, Farish M, Donbavand J, Pickup HE. 2005. Breed, litter and parity effects on placental weight and placentome number and consequences for the neonatal behavior of the lamb. Theriogenology, 63: 1092-1110.
- Echternkamp SE: 1993. Relationship between placental development and calf birth weight in beef cattle. Anim Reprod Sci, 32; 1–13.
Ayrıntılar
Birincil Dil
İngilizce
Konular
Mühendislik
Bölüm
Araştırma Makalesi
Yazarlar
Cem Tırınk
*
0000-0001-6902-5837
Türkiye
Yayımlanma Tarihi
1 Ekim 2020
Gönderilme Tarihi
1 Eylül 2020
Kabul Tarihi
10 Eylül 2020
Yayımlandığı Sayı
Yıl 2020 Cilt: 3 Sayı: 4
Cited By
Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed
PLOS ONE
https://doi.org/10.1371/journal.pone.0289348Estimation of Body Weight Based on Biometric Measurements by Using Random Forest Regression, Support Vector Regression and CART Algorithms
Animals
https://doi.org/10.3390/ani13050798