Research Article
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Determination of Outliers in Growing Quail's Data with Different Sample Size

Year 2017, Volume: 21 Issue: 1, 99 - 111, 25.03.2017
https://doi.org/10.29050/harranziraat.303175

Abstract

The aim of this study was to use as an
alternative to MSS estimators M of Robust Regression estimators method is to
examine the outlier in Japanese quail body weight data. During 15 weeks in the
study, body weight measurements of 150 Japanese quails were recorded weekly. To
determine the effect of outliers, quails were randomly divided into three
groups and 10, 20 and 30 samplings were performed from each group,
respectively. To conclude, it was concluded that the estimator M of outliers on
the results of estimation methods can be used with success in this regard.
Also, the number of samples increases that marred the outliers was identified
and therefore they cannot emerge.

References

  • Akdeniz, F., 1998. Olasılık ve istatistik, Baki Kitapevi, Adana, Turkey.
  • Balcioglu, M. S., Karabag, K., Yolcu, H. I.,Sahin, E.,2005. Japon bıldırcınlarında canlı ağırlığa göre iki yönlü seleksiyonun eşeysel olgunluk yaşı ve bazı verim özellikleri üzerine etkisi. GAP. IV. Tarım Kongresi, 21-23 Eylül, Sanliurfa, Turkey.
  • Bek, Y.,Efe, E.,1987. Araştırma deneme metotları 1, C.U. Ziraat Fakültesi Ofset ve Teksir Atölyesi, Adana.
  • Coelho, D. T., Dale, R. F., 1980. An energy-crop growth variable and temperature function for predicting corn growth and development: Planting to silking. Agronomy Journal, 72: 503-510.
  • Cohen, J., 1992. Statistical power analysis. Current Directions in Psychological Science, 1: 98-101.
  • Cook, R. D., 1997. Detection of infuential observations in linear regression. Technometrics, 19: 15-18.
  • D’agostino, R. B, Stephens, M. A., 1986. Goodness of fit techniques. Marcel Dekker Inc., New York.
  • Davies, P. L., Gather, U., 1993. The identification of multiple outliers (with discussion), Journal of Statistical Planning and Inference, 122: 65-78.
  • Davis, R. A., Knight, K., Liu, J., 1991. M-estimation for autoregressions with infinite variance. Stochastic Processes and their Applications, 40: 145-180.
  • Ergunes, E., 2004. En küçük kareler yöntemi ile ridge regresyon yönteminin karşılaştırılmalı olarak incelenmesi. C.U. Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, Adana.
  • Gurcan, E. K., Cobanoglu, O., Genc, S., 2012. Determination of body weight-age relationship by non-linear models in Japanese quail. Journal of Animal and Veterinary Advances, 11: 314-317.
  • Hadi, A. S., Simonoff, J. S., 1993. Procedures for the identification of multiple outliers in linear models. Journal of the American Statistical Association, 88: 1264–1272.
  • Hampel, F. R., 1973. Robust estimation: A condensed partial survey. Zeitschrift fur Wahrscheinlichkeitstheorie und verwandte Gebiete, 27: 87-104.
  • Hancock, G. R., Buehl, M. M., 2008. Second-order latent growth models with shifting indicators. Journal of Modern Applied Statistical Methods, 7: 39-55.
  • Hawkins, D. M., Bradu, D., Kass, G. V., 1984. Location of several outliers in multiple-regression data using elemental sets. Technometrics, 26: 197–208.
  • Huber, P. J., 2005. Robust Statistics. New York, John Wiley and Sons, 1981.
  • Huber, P. J., Dutter, R., 1974. Numerical solutions of robust regression problems, in: G. Brickmann, ed., COMPSTAT 1974 (Physika Verlag, Wein): 165-172.
  • Karadavut, U., Genc, A., Tozluca, A., Kinaci, I., Aksoyak, S., Palta, C., Pekgor, A., 2005. Nohut (Cicerarietinum L.) bitkisinde verime etki eden bazı karakterlerin alternative regresyon yöntemleriyle karşılaştırılması. Tarım Bilimleri Dergisi, 11: 328-333.
  • Karadavut, U., Taskin, A., 2014. Estimation of heritability of weight gain of Japanese quail by using analysis of variance, maximum and restricted likelihood tests. Türk Tarım ve Doğa Bilimleri Dergisi, 1: 59–63.
  • Kocak, C., Ozkan, S., 2000. Bıldırcın, sülün ve keklik yetiştiriciliği. Ege Üniversitesi Ziraat Fakültesi Yayınları. No: 538, Izmir.
  • Liu, H., Sirish, J. W., 2004. On-line outlier detection and data cleaning. Computers & Chemical Engineering, 28: 1635–1647.
  • Liu, M., Hancock, G. R., Harring, J. R., 2011. Using finite mixture modelling to deal with systematic measurement error: A case study. Journal of Modern Applied Statistical Methods, 10: 249-261.
  • Marchette, D. J., Solka, J. L., 2003. Using data images for outlier detection. Comput. Statist., 43: 541-552.
  • Minvielle, F., 2004. The future of Japanese quail for research and production. World's Poultry Science Journal, 60: 500–507.
  • Narinc, D., Aksoy, T., Karaman, E., Curek, D. I., 2010. Analysis of fitting growth models in medium growing chicken raised indoor system. Trends in Animal and Veterinary Sciences, 1: 12-18.
  • Quackenbush, J., 2002. Microarray data normalization and transformation. Nature Genetics, 32: 496-501.
  • Ramsay, T., Elkum, N. A., 2005. Comparison of four different methods for outlier detection in bioequivalence studies. Journal of Biopharmaceutical Statistics, 15: 43-52.
  • Rousseeuw, P. J., Leroy, A. M., 1987. Robust regression and outlier detection. New York, John Wiley.
  • Rousseeuw, P. J., Yohai, V., 1984. Robust regression by means of S-estimators. Lecture Notes in Statistics, 26: 256-272.
  • Sahinler, S., 1997. Regresyon analizinde etkili gözlemlerin (Influential Observations) belirlenmesinde kullanılan istatistiklerin karşılaştırmalı olarak incelenmesi, Ç.Ü. Fen Bilimleri Enstitüsü, Zootekni Anabilim Dalı, Doktora Tezi, Adana.
  • Satman, M. H., 2005. Doğrusal regresyonda aykırı gözlemlerin teşhis yöntemleri. İstanbul Üniversitesi, Sosyal Bilimler Enstitüsü, Ekonometri Anabilim Dalı, Yüksek Lisans Tezi, İstanbul.
  • Seber, G. A. F., 1984. Multivariate observations. New York, John Wiley and Sons.
  • Stromberg, A. J., Hossjer, O., Hawkins, D. M., 2000. The least trimmed differences regression estimator and alternatives. Journal of the American Statistical Association, 95: 853-864.
  • Testik, A., Uluocak, N., Sarica, M., 1993. Değişik genotiplerdeki Japon bıldırcınlarının (Coturnix coturnix japonica) bazı verim özellikleri. Turkish Journal of Veterinary and Animal Sciences, 17: 167-173.
  • Tserveni-Gousi, A. S., 1987. Relationship between parental age, egg weight and hatching weight of Japanese quail. British Poultry Science, 28: 749-752.
  • Wang, W., Chow, S. C., 2003. Examining outlying subjects and outlying records in bioequivalence trials. Journal of Biopharmaceutical Statistics, 13: 43-56.
  • Willemsen, H., Everaert, N., Witters, A., Smith, L., Debonne, M., Verschuere, F., Garain, P., Berckmans, D., Decuypere, E., Bruggeman, V., 2008. Critical assessment of chick quality measurements as indicator of post hatch performance. Poultry Science, 87: 2358-2366.
  • Wisnowski, J. W. Montgomery, D. C., Simpson, J. R. A., 2001. Comparative analysis of multiple outlier detection procedures in the linear regression model. Computational Statistics, 36: 351–382.
  • Yildirim, N., 2010. Determination the effects of outliers at the least squares, ridge regression and robust Regression analysis results. Ç.Ü. Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, Adana.

Bıldırcın Büyüme Verilerinde Farklı Örnek Büyüklüklerinin Aykırı Değerlerinin Belirlenmesi

Year 2017, Volume: 21 Issue: 1, 99 - 111, 25.03.2017
https://doi.org/10.29050/harranziraat.303175

Abstract

Bu çalışmanın amacı, Japon bıldırcını vücut ağırlığı
verilerinde aykırı değerleri incelemektir. Robust Regresyon tahmin yöntemi olan
M tahmin yönteminin MSS alternatifi olarak kullanmaktır. Çalışmada 15 hafta
boyunca, 150 Japon bıldırcınının vücut ağırlığı ölçümleri haftalık olarak
kaydedilmiştir. Aykırı değer etkisini belirlemek için, bıldırcınlar rastgele üç
gruba ayrılmış ve her bir gruptan 10, 20 ve 30 örnekleme yapılmıştır. Sonuç
olarak, bu tahmin yöntemlerinin sonuçlarına göre M aykırı değer tahmin
edicisinin bu konuda başarı ile kullanılabileceği sonucuna varılmıştır.

References

  • Akdeniz, F., 1998. Olasılık ve istatistik, Baki Kitapevi, Adana, Turkey.
  • Balcioglu, M. S., Karabag, K., Yolcu, H. I.,Sahin, E.,2005. Japon bıldırcınlarında canlı ağırlığa göre iki yönlü seleksiyonun eşeysel olgunluk yaşı ve bazı verim özellikleri üzerine etkisi. GAP. IV. Tarım Kongresi, 21-23 Eylül, Sanliurfa, Turkey.
  • Bek, Y.,Efe, E.,1987. Araştırma deneme metotları 1, C.U. Ziraat Fakültesi Ofset ve Teksir Atölyesi, Adana.
  • Coelho, D. T., Dale, R. F., 1980. An energy-crop growth variable and temperature function for predicting corn growth and development: Planting to silking. Agronomy Journal, 72: 503-510.
  • Cohen, J., 1992. Statistical power analysis. Current Directions in Psychological Science, 1: 98-101.
  • Cook, R. D., 1997. Detection of infuential observations in linear regression. Technometrics, 19: 15-18.
  • D’agostino, R. B, Stephens, M. A., 1986. Goodness of fit techniques. Marcel Dekker Inc., New York.
  • Davies, P. L., Gather, U., 1993. The identification of multiple outliers (with discussion), Journal of Statistical Planning and Inference, 122: 65-78.
  • Davis, R. A., Knight, K., Liu, J., 1991. M-estimation for autoregressions with infinite variance. Stochastic Processes and their Applications, 40: 145-180.
  • Ergunes, E., 2004. En küçük kareler yöntemi ile ridge regresyon yönteminin karşılaştırılmalı olarak incelenmesi. C.U. Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, Adana.
  • Gurcan, E. K., Cobanoglu, O., Genc, S., 2012. Determination of body weight-age relationship by non-linear models in Japanese quail. Journal of Animal and Veterinary Advances, 11: 314-317.
  • Hadi, A. S., Simonoff, J. S., 1993. Procedures for the identification of multiple outliers in linear models. Journal of the American Statistical Association, 88: 1264–1272.
  • Hampel, F. R., 1973. Robust estimation: A condensed partial survey. Zeitschrift fur Wahrscheinlichkeitstheorie und verwandte Gebiete, 27: 87-104.
  • Hancock, G. R., Buehl, M. M., 2008. Second-order latent growth models with shifting indicators. Journal of Modern Applied Statistical Methods, 7: 39-55.
  • Hawkins, D. M., Bradu, D., Kass, G. V., 1984. Location of several outliers in multiple-regression data using elemental sets. Technometrics, 26: 197–208.
  • Huber, P. J., 2005. Robust Statistics. New York, John Wiley and Sons, 1981.
  • Huber, P. J., Dutter, R., 1974. Numerical solutions of robust regression problems, in: G. Brickmann, ed., COMPSTAT 1974 (Physika Verlag, Wein): 165-172.
  • Karadavut, U., Genc, A., Tozluca, A., Kinaci, I., Aksoyak, S., Palta, C., Pekgor, A., 2005. Nohut (Cicerarietinum L.) bitkisinde verime etki eden bazı karakterlerin alternative regresyon yöntemleriyle karşılaştırılması. Tarım Bilimleri Dergisi, 11: 328-333.
  • Karadavut, U., Taskin, A., 2014. Estimation of heritability of weight gain of Japanese quail by using analysis of variance, maximum and restricted likelihood tests. Türk Tarım ve Doğa Bilimleri Dergisi, 1: 59–63.
  • Kocak, C., Ozkan, S., 2000. Bıldırcın, sülün ve keklik yetiştiriciliği. Ege Üniversitesi Ziraat Fakültesi Yayınları. No: 538, Izmir.
  • Liu, H., Sirish, J. W., 2004. On-line outlier detection and data cleaning. Computers & Chemical Engineering, 28: 1635–1647.
  • Liu, M., Hancock, G. R., Harring, J. R., 2011. Using finite mixture modelling to deal with systematic measurement error: A case study. Journal of Modern Applied Statistical Methods, 10: 249-261.
  • Marchette, D. J., Solka, J. L., 2003. Using data images for outlier detection. Comput. Statist., 43: 541-552.
  • Minvielle, F., 2004. The future of Japanese quail for research and production. World's Poultry Science Journal, 60: 500–507.
  • Narinc, D., Aksoy, T., Karaman, E., Curek, D. I., 2010. Analysis of fitting growth models in medium growing chicken raised indoor system. Trends in Animal and Veterinary Sciences, 1: 12-18.
  • Quackenbush, J., 2002. Microarray data normalization and transformation. Nature Genetics, 32: 496-501.
  • Ramsay, T., Elkum, N. A., 2005. Comparison of four different methods for outlier detection in bioequivalence studies. Journal of Biopharmaceutical Statistics, 15: 43-52.
  • Rousseeuw, P. J., Leroy, A. M., 1987. Robust regression and outlier detection. New York, John Wiley.
  • Rousseeuw, P. J., Yohai, V., 1984. Robust regression by means of S-estimators. Lecture Notes in Statistics, 26: 256-272.
  • Sahinler, S., 1997. Regresyon analizinde etkili gözlemlerin (Influential Observations) belirlenmesinde kullanılan istatistiklerin karşılaştırmalı olarak incelenmesi, Ç.Ü. Fen Bilimleri Enstitüsü, Zootekni Anabilim Dalı, Doktora Tezi, Adana.
  • Satman, M. H., 2005. Doğrusal regresyonda aykırı gözlemlerin teşhis yöntemleri. İstanbul Üniversitesi, Sosyal Bilimler Enstitüsü, Ekonometri Anabilim Dalı, Yüksek Lisans Tezi, İstanbul.
  • Seber, G. A. F., 1984. Multivariate observations. New York, John Wiley and Sons.
  • Stromberg, A. J., Hossjer, O., Hawkins, D. M., 2000. The least trimmed differences regression estimator and alternatives. Journal of the American Statistical Association, 95: 853-864.
  • Testik, A., Uluocak, N., Sarica, M., 1993. Değişik genotiplerdeki Japon bıldırcınlarının (Coturnix coturnix japonica) bazı verim özellikleri. Turkish Journal of Veterinary and Animal Sciences, 17: 167-173.
  • Tserveni-Gousi, A. S., 1987. Relationship between parental age, egg weight and hatching weight of Japanese quail. British Poultry Science, 28: 749-752.
  • Wang, W., Chow, S. C., 2003. Examining outlying subjects and outlying records in bioequivalence trials. Journal of Biopharmaceutical Statistics, 13: 43-56.
  • Willemsen, H., Everaert, N., Witters, A., Smith, L., Debonne, M., Verschuere, F., Garain, P., Berckmans, D., Decuypere, E., Bruggeman, V., 2008. Critical assessment of chick quality measurements as indicator of post hatch performance. Poultry Science, 87: 2358-2366.
  • Wisnowski, J. W. Montgomery, D. C., Simpson, J. R. A., 2001. Comparative analysis of multiple outlier detection procedures in the linear regression model. Computational Statistics, 36: 351–382.
  • Yildirim, N., 2010. Determination the effects of outliers at the least squares, ridge regression and robust Regression analysis results. Ç.Ü. Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, Adana.
There are 39 citations in total.

Details

Subjects Agricultural Engineering
Journal Section dp
Authors

Ufuk Karadavut

Atilla Taşkın This is me

Publication Date March 25, 2017
Submission Date October 28, 2016
Published in Issue Year 2017 Volume: 21 Issue: 1

Cite

APA Karadavut, U., & Taşkın, A. (2017). Determination of Outliers in Growing Quail’s Data with Different Sample Size. Harran Tarım Ve Gıda Bilimleri Dergisi, 21(1), 99-111. https://doi.org/10.29050/harranziraat.303175

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