1. Acquaah, G. (2009). Principles of Plant Genetics and Breeding. John Wiley & Sons, New Jersey, USA.

2. Aitkin, M. (1978). The analysis of unbalanced cross-classifications. Journal of the Royal Statistical Society Series A (General), 141: 195-223. [DOI:10.2307/2344453]

3. Akbarpour, O., Dehghani, H., Rousta, M., J, and Amini, A. (2015a). Evaluation of some properties of Iranian wheat genotypes in normal and salt-stressed conditions using Restricted Maximum Likelihood (REML). Iranian Journal of Field Crop Science, 46: 57-69 (In Persian).

4. Akbarpour, O., Dehghani, H. and Rousta, M.J. (2015b). Evaluation of salt stress of Iranian wheat germplasm under field conditions. Crop and Pasture Science, 66: 770-781. [DOI:10.1071/CP14286]

5. Anderson, R.L. and Bancroft, T.A. (1952). Statistical Theory in Research. McGraw-Hill Book Company, Inc, New York, USA.

6. Barnett, V. (1966). Evaluation of the maximum-likelihood estimator where the likelihood equation has multiple roots. Biometrika, 53: 151-165. [DOI:10.1093/biomet/53.1-2.151]

7. Bertsekas, D.P. and Tsitsiklis, J.N. (2008). Introduction to Probability. American Mathematical Society Press, Providence, USA.

8. Bishop, C. (1992). Exact Ccalculation of the Hessian matrix for the Multilayer Perceptron. MIT Press, Massachusetts, USA.

9. Casella, G. and Berger, R.L. (1990). Statistical Inference. Cole Advanced Books & Software, Pacific Grove, California, USA.

10. Der, G. and Everitt, B. (2002). A handbook of statistical analyses using SAS, Chapman and Hall, London, UK.

11. Falconer, D. and Mackay, T. (1996). Introduction to Quantitative Genetics. Longman, London, UK.

12. Fisher, R.A. (1925). Statistical Methods for Research Workers. Oliver and Boyd, Edinburgh, London, UK.

13. Graybill, F.A. and Iyer, H.K. (1994). Regression analysis: Concepts and Applications, Belmont, California, USA.

14. Graybill, F.A. and Wortham, A. (1956). A note on uniformly best unbiased estimators for variance components. Journal of the American Statistical Association, 51: 266-268. [DOI:10.1080/01621459.1956.10501325]

15. Hallauer, A.R., Carena, M.J. and Miranda Filho, J.d. (2010). Quantitative Genetics in Maize Breeding, Springer Science & Business Media, Berlin, DE.

16. Hartley, H.O. and Rao, J.N. (1967). Maximum-likelihood estimation for the mixed analysis of variance model. Biometrika, 54: 93-108. [DOI:10.1093/biomet/54.1-2.93]

17. Harville, D.A. (1977). Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association, 72: 320-338. [DOI:10.1080/01621459.1977.10480998]

18. Henderson, C.R. (1984). Application of Linear Models in Animal Breeding. University of Guelph, Ontario, California, USA.

19. Henderson, C., Searle, S. and Schaeffer, L. (1974). The invariance and calculation of method 2 for estimating variance components. Biometrics, 30: 583-588. [DOI:10.2307/2529223]

20. Holland, J.B., Nyquist, W.E. and Cervantes-Martínez, C.T. (2003). Estimating and interpreting heritability for plant breeding: an update. Plant Breeding Reviews, 22: 9-112.

21. Ismaili, A., Karami, F., Akbarpour, O. and Rezaei Nejad, A. (2016). Estimation of Genotypic Correlation and Heritability of Apricot Traits, Using Restricted Maximum Likelihood in Repeated Measures Data. Canadian Journal of Plant Science, 96: 439-447. [DOI:10.1139/cjps-2015-0253]

22. Kempthorne, O. (1968). Discussion of Searle. Biometrics, 24: 782-784.

23. King, G. (1998). Unifying Political Methodology: The Likelihood Theory of Statistical Inference. Cam- bridge University Press, Londan, UK. [DOI:10.3998/mpub.23784]

24. Lehmann, E.L. and Casella, G. (2006). Theory of Point Estimation. Springer Science & Business Media, Berlin, DE.

25. Littell, R., Milliken, G., Stroup, W. and Wolfinger, R. (2006). SAS system for Mixed Models. SAS Institute Inc, Cary, North Carolina, USA.

26. Lynch, M. and Walsh, B. (1998). Genetics and analysis of Quantitative Traits. Sinauer Associates, Massachusetts, USA.

27. Milliken, G.A. and Johnson, D.E. (1992). Analysis of Messy Data. Volume I: Designed experiments., Chapman & Hall, New York, USA.

28. Mood, A.M., Graybill, F.A. and Boes, D.C. (1974). Introduction to the Theory of Statistics. 3rd ed, USA.

29. Nelder, J. (1977). A reformulation of linear models. Journal of the Royal Statistical Society Series A (General), 140: 48-77. [DOI:10.2307/2344517]

30. Neter, J., Kutner, M.H., Nachtsheim, C.J. and Wasserman, W. (2004). Applied Linear Statistical Models, Irwin Chicago, USA.

31. Nyquist, W.E. and Baker, R. (1991). Estimation of heritability and prediction of selection response in plant populations. Critical Reviews in Plant Sciences, 10: 235-322. [DOI:10.1080/07352689109382313]

32. Patterson, H.D. and Thompson, R. (1971). Recovery of inter-block information when block sizes are unequal. Biometrika, 58(3): 545-554. [DOI:10.1093/biomet/58.3.545]

33. Rawlings, J.O., Pantula, S.G. and Dickey, D.A. (2001). Applied Regression Analysis: A Research Tool. Springer Science & Business Media, Berlin, DE.

34. Sahai, H. and Miguel, M.O. (2004). Analysis of Variance for Random Models Volume I: Balanced Data Theory, Methods, Applications and Data Analysis. Business Media New York, USA. [DOI:10.1007/978-0-8176-8168-5]

35. Sahai, H. and Ojeda, M.M. (2004). Analysis of Variance for Random Models, Volume 2: Unbalanced Data: Theory, Methods, Applications, and Data Analysis, Springer Science & Business Media, Berlin, DE.

36. Sahai, H. and Thompson, W.O. (1973). The Teacher's Corner: Non-Negative Maximum Likelihood Estimators of Variance Components in a Simple Linear Model. The American Statistician, 27: 112-113. [DOI:10.1080/00031305.1973.10479006]

37. Schaeffer, L. (1998). Variance Component Estimation Course Notes. University of New England, Armidale, NSW.

38. Searle, S., Casella, G. and McCulloch, C. (2006). Variance Components. John Wiley and Sons, New York, USA.

39. Searle, S.R. (1971). Linear Models, John Wiley & Sons, New Jersey, USA.

40. Small, C.G., Wang, J. and Yang, Z. (2000). Eliminating multiple root problems in estimation Statistical Science, 15: 313-341. [DOI:10.1214/ss/1009213000]

41. Sorensen, D. and Gianola, D. (2007). Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics, Springer Science & Business Media, Berlin, DE.

42. Steel, R. and Torrie, J. (1997). Principles and Procedures of Statistics. A Biometrical Approach, McGraw-Hill Book Company In Company, New York, USA.

43. Thompson, W. and Moore, J.R. (1963). Non-negative estimates of variance components. Technometrics, 5: 441-449. [DOI:10.1080/00401706.1963.10490122]

44. Valizadeh, S. (2014). Evaluation of genotypic variation of wheat genotypes under low water stress in Khorramabad climate conditions. Lorestan Univarsity, Lorestan, IR.

45. Yang, R.C. (2010). Towards understanding and use of mixed-model analysis of agricultural experiments. Canadian Journal of Plant Science, 90: 605-627. [DOI:10.4141/CJPS10049]