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In: Financial Sector Development Studies, No.7-2021, Arab Monetary Fund, May 2021
SSRN
In: Statistica Neerlandica: journal of the Netherlands Society for Statistics and Operations Research, Band 56, Heft 3, S. 330-345
ISSN: 1467-9574
ML–estimation of regression parameters with incomplete covariate information usually requires a distributional assumption regarding the concerned covariates that implies a source of misspecification. Semiparametric procedures avoid such assumptions at the expense of efficiency. In this paper a simulation study with small sample size is carried out to get an idea of the performance of the ML–estimator under misspecification and to compare it with the semiparametric procedures when the former is based on a correct assumption. The results show that there is only a little gain by correct parametric assumptions, which does not justify the possibly large bias when the assumptions are not met. Additionally, a simple modification of the complete case estimator appears to be nearly semiparametric efficient.
In: Journal of economics, Band 72, Heft 2, S. 129-152
ISSN: 1617-7134
In: Statistical papers, Band 36, Heft 1, S. 41-47
ISSN: 1613-9798
In: Kölner Zeitschrift für Soziologie und Sozialpsychologie: KZfSS, Band 46, Heft 2, S. 355-357
ISSN: 0023-2653
In: Routledge revivals
In: Communications in statistics. Simulation and computation, Band 51, Heft 4, S. 1578-1590
ISSN: 1532-4141
In: Communications in statistics. Theory and methods, Band 48, Heft 4, S. 850-864
ISSN: 1532-415X
In: Epistemological Aspects of Computer Simulation in the Social Sciences; Lecture Notes in Computer Science, S. 48-59
In: Journal of the Indian Society for Probability and Statistics: JISPS, Band 24, Heft 1, S. 23-54
ISSN: 2364-9569
In: OIDA International Journal of Sustainable Development, Band 7, Heft No.03, S. 35-42
SSRN