JIN, Qinqin, LIAO, Tianzi, PENG, Wei, WANG, Jia und LIU, Bin, 2026. Mixed smoothly clipped absolute deviation estimator for stochastic restricted regression models. Communications in Statistics: Theory & Methods. 1 März 2026. Vol. 55, no. 5, p. 1477-1491. DOI 10.1080/03610926.2025.2526659.
Elsevier - Harvard (with titles)Jin, Q., Liao, T., Peng, W., Wang, J., Liu, B., 2026. Mixed smoothly clipped absolute deviation estimator for stochastic restricted regression models. Communications in Statistics: Theory & Methods 55, 1477-1491. https://doi.org/10.1080/03610926.2025.2526659
American Psychological Association 7th editionJin, Q., Liao, T., Peng, W., Wang, J., & Liu, B. (2026). Mixed smoothly clipped absolute deviation estimator for stochastic restricted regression models. Communications in Statistics: Theory & Methods, 55(5), 1477-1491. https://doi.org/10.1080/03610926.2025.2526659
Springer - Basic (author-date)Jin Q, Liao T, Peng W, Wang J, Liu B (2026) Mixed smoothly clipped absolute deviation estimator for stochastic restricted regression models.. Communications in Statistics: Theory & Methods 55:1477-1491. https://doi.org/10.1080/03610926.2025.2526659
Juristische Zitierweise (Stüber) (Deutsch)Jin, Qinqin/ Liao, Tianzi/ Peng, Wei/ Wang, Jia/ Liu, Bin, Mixed smoothly clipped absolute deviation estimator for stochastic restricted regression models., Communications in Statistics: Theory & Methods 2026, 1477-1491.