Multi-response Regression Modeling for an Agricultural Experiment
DOI:
https://doi.org/10.21928/juhd.v4n2y2018.pp46-52Abstract
During building model process, it is difficult to construct a multiple regression model (MRM) while the response variable(Y) is proposed as a vector of (r.v), (Y1,Y2,Y3,…..,Yn) in an experiment. So that a single response (MRM) is not able to perform multi-response data (MRD) separately (one for each response), this is because of the linear dependency (LD) among responses, then (MRRM) which was proposed by (Len Beirman, & Freidman 1997) has better performance to detect effects and patterns for the factors, (Explanatory) (X1 , X2, X3 ,…..Xk )that are introduced to the (MRRM) system on the (r.v) altogether. This Study was applied (MRRM) on an agricultural experiment through (450m2) in west Sulaimani- Kurdistan Region-Iraq.
References
Chow, G. C. (1960). “Tests of equality between sets of coefficients in two linear regressions”. Econometrica, 28, 591–605.
Dobson, Annette J. “An Introduction to Generalized Linear Models”. 2nd edition, (Chapman & Hall/CRC texts in statistical science series). Includes bibliographical references and index. ISBN 1-58488-165-8 (alk. paper), 2002.
Grabill, F. A. (1961). “An Introduction to Linear Statistical Models”, Vol. I, McGraw- Hill Book Company, Inc. New York.
Jump up_ Pearl, J. (2000). “Causality: Models, Reasoning, and Inference”. New York: Cambridge University Press. ISBN 0521773628.
Khuri, A. I. (1985). A Test for Lack of Fit of a Linear Multiresponse Model, Technometrics.
Khuri, A. I. and Conlon, M (1981). “Simultaneous optimization of Multiple Responses Represented by Polynomial Regression Functions”, American Statistical Association and American Society for Quality.
Kumar, V. and Pant, A. K. (2011). “Statistical Analysis of Multi-response Plant Growth Data”, Int. J. Agriculture. Stat. Sci., Vol. 7, No. 1. 289-300.
Liao, H. c. (2004). “A Data Envelopment Analysis Method For optimizing Multi- Response Problem with Censored Data in the Taguchi Method”, Technical Report, Department of Health Services Administration, Chung-Shan Medical University.
Manelwijesinha Khuri .A.I (1987) “The Sequential Generation of Multi-response d- optimal Design When the Variance-Covariance Matrix is Not Known”. American Statistical Association and American Society for Quality.
Ren, S. and Kim, H. (2003). “Comparative Assessment of Multi-response Regression Methods for Predicting the Mechanisms of Toxic Action of Phenols”, J. Chem. Inf. Computer. Sci., 43(6), 2106-2110.
Seber, G. A. F. (1984). Multivariate Observations, John Wiley, New York. Shroff. R. (2000), Chairman's address in Pesticide information annual issue. (New Delhi: Pesticide association of Indian Publication).
Simila.T,.and Tikka, J. (2007).“Input Selection and Shrinkage in Multi-response Linear Regression”, Technical Report, Laboratory of Computer and Information Science, Helsinki University of Technology, Helsinki, Finland.
Stewart, W. E., Caracotsios M. and Sorensen J. P. (1992). “Parameter Estimation from Multi-response Data”, Dept. Of Chemical Engineering, University of Wisconsin, Madison
Dobson, Annette J. “An Introduction to Generalized Linear Models”. 2nd edition, (Chapman & Hall/CRC texts in statistical science series). Includes bibliographical references and index. ISBN 1-58488-165-8 (alk. paper), 2002.
Grabill, F. A. (1961). “An Introduction to Linear Statistical Models”, Vol. I, McGraw- Hill Book Company, Inc. New York.
Jump up_ Pearl, J. (2000). “Causality: Models, Reasoning, and Inference”. New York: Cambridge University Press. ISBN 0521773628.
Khuri, A. I. (1985). A Test for Lack of Fit of a Linear Multiresponse Model, Technometrics.
Khuri, A. I. and Conlon, M (1981). “Simultaneous optimization of Multiple Responses Represented by Polynomial Regression Functions”, American Statistical Association and American Society for Quality.
Kumar, V. and Pant, A. K. (2011). “Statistical Analysis of Multi-response Plant Growth Data”, Int. J. Agriculture. Stat. Sci., Vol. 7, No. 1. 289-300.
Liao, H. c. (2004). “A Data Envelopment Analysis Method For optimizing Multi- Response Problem with Censored Data in the Taguchi Method”, Technical Report, Department of Health Services Administration, Chung-Shan Medical University.
Manelwijesinha Khuri .A.I (1987) “The Sequential Generation of Multi-response d- optimal Design When the Variance-Covariance Matrix is Not Known”. American Statistical Association and American Society for Quality.
Ren, S. and Kim, H. (2003). “Comparative Assessment of Multi-response Regression Methods for Predicting the Mechanisms of Toxic Action of Phenols”, J. Chem. Inf. Computer. Sci., 43(6), 2106-2110.
Seber, G. A. F. (1984). Multivariate Observations, John Wiley, New York. Shroff. R. (2000), Chairman's address in Pesticide information annual issue. (New Delhi: Pesticide association of Indian Publication).
Simila.T,.and Tikka, J. (2007).“Input Selection and Shrinkage in Multi-response Linear Regression”, Technical Report, Laboratory of Computer and Information Science, Helsinki University of Technology, Helsinki, Finland.
Stewart, W. E., Caracotsios M. and Sorensen J. P. (1992). “Parameter Estimation from Multi-response Data”, Dept. Of Chemical Engineering, University of Wisconsin, Madison
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Published
2018-06-30
How to Cite
Ahmed, N. M., & Taher, H. A. (2018). Multi-response Regression Modeling for an Agricultural Experiment. Journal of University of Human Development, 4(2), 46–52. https://doi.org/10.21928/juhd.v4n2y2018.pp46-52
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