References
- P. Billingsley, Convergence of Probability Measures, Wiley, New York, 1968.
- T. Birkel, A functional central limit theorem for positively dependent random variables, J. Multivariate Anal. 11 (1993), 314-320.
- J. T. Cox and G. Grimmett, Central limit theorems for associated random variables and the percolation model, Ann. Probab. 12 (1984), 514-528. https://doi.org/10.1214/aop/1176993303
- D. Kafles and R. M. Bhaskara, Weak consistency of least squares estimators in linear models, J. Multivariate Anal. 12 (1982), 186-198. https://doi.org/10.1016/0047-259X(82)90014-8
- E. L. Lehmann, Some concepts of dependence, Ann. Math. Stat. 37 (1966), 1137-1153. https://doi.org/10.1214/aoms/1177699260
- C. M. Newman, Normal fluctuations and the FKG inequalities, Commun. Math. Phys. 74 (1980), 119-128. https://doi.org/10.1007/BF01197754
- C. M. Newman, Asymptotic independence and limit theorems for positively and negatively dependent random variables, in Inequalities in Statistics and Probability (Tong, Y.L., ed., IMS, Hayward, CA), 1984.
- M. Peligrad and S. Utev, Central limit theorem for linear processes, Ann. Probab. 25 (1997), 443-456. https://doi.org/10.1214/aop/1024404295
- M. B. Priestley and M. T. Chao, Nonparametric function fitting, J. Roy. Statist. Soc. Ser. B 34 (1972), 385-392.
- C. R. Rao and M. T. Zhao, Linear representation of M-estimates in linear models, Canad. J. Statist. 20 (1992), 359-368. https://doi.org/10.2307/3315607
- G. Roussas, Asymptotic normality of random fields of positively or negatively associated processes, J. Multivariate Anal. 50 (1994), 152-173. https://doi.org/10.1006/jmva.1994.1039
- S. Shanskin, A strong invariance principle for postively or negatively associated random fields, Statist. Probab. Lett. 78 (2008), 2121-2129. https://doi.org/10.1016/j.spl.2008.01.078