МАЪЛУМОТЛАР ТАСОДИФИЙ ЙЎҚОЛГАН ЧИЗИҚЛИ РЕГРЕССИЯ: БООТСТРАП ЁНДАШУВИ

Mualliflar

DOI:

https://doi.org/10.60078/2992-877X-2024-vol2-iss4-pp492-502

Annotasiya

ОЛС регрессиялари нуқта ва интервалларни холис ва самарали баҳолаш учун бир қатор фаразларга эга. Тасодифий йўқолган маълумотлар (МНАР) чизиқли регрессияни баҳолашда жиддий муаммоларни келтириб чиқариши мумкин. Ушбу тадққотда биз МНАР маълумотлари билан ОЛС ишонч оралиғи баҳоларининг ишлашини баҳолаймиз. Биз, шунингдек, бундай маълумотлар ҳолатлари учун восита сифатида юклашни таклиф қиламиз ва анъанавий ишонч оралиқларини боотстрап билан солиштирамиз. Ҳақиқий параметрларни билишимиз кераклиги сабабли, биз симуляция тадқиқотини ўтказамиз. Тадқиқот натижалари шуни кўрсатадики, иккала ёндашув ҳам ўхшаш оралиқ ўлчамига эга ўхшаш натижаларни кўрсатади. Боотстрап жуда кўп ҳисоб-китобларни талаб қилишини ҳисобга олиб, анъанавий усулларни МНАР ҳолатида ҳам қўллаш тавсия этилади.

Kalit so‘zlar:

чизиқли модел намуна ўлчами ишонч интервал юклаш чизиғи аниқлик интервал ўлчами тасодифий эмас

Bibliografik manbalar

Carpenter, J. R., & Kenward, M. G. (2012). Missing data in clinical trials: a practical guide. Practical Guides to Biostatistics and Epidemiology. Cambridge University Press.

Chernick, M. R., and LaBudde, R. A. (2014). An introduction to bootstrap methods with applications to R. John Wiley & Sons.

Chernozhukov, V., and Hong, H. (2003). An MCMC approach to classical estimation. Journal of Econometrics, 115(2), 293-346.

Davison , A. C. , and Hinkley , D. V. (1997). Bootstrap Methods and Their Applications. Cambridge University Press, Cambridge .

DiCiccio , T., and Efron , B. (1992). More accurate confidence intervals in exponential families. Biometrika 79, 231 – 245 .

Efron , B., and Tibshirani , R. (1986). Bootstrap methods for standard errors, confidence intervals and other measures of statistical accuracy. Statistical Science. Vol. 1 , 54 – 77

Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1-26.

Efron, B. (1982). The Jackknife, the Bootstrap and Other Resampling Plans. SIAM, Philadelphia

Fan, Y., and Li, Q. (2004). A consistent model specification test based on the kernel density estimation. Econometrica, 72(6), 1845-1858.

Flachaire, E. (2007). Bootstrapping heteroscedastic regression models: wild bootstrap vs pairs bootstrap. Computational Statistics and Data Analysis, 49 (2), 361-376

Freedman , D. A. (1981). Bootstrapping regression models. Annals of Statistics, 9, 1218 – 1228

Graham, J. W. (2003). Adding missing-data-relevant variables to FIML-based structural equation models. Structural Equation Modeling, 10(1), 80-100.

Greene, W. H. (2021) Econometric Analysis, 8th edn, Pearson

Gujarati, D. N., Porter, D. C., and Gunasekar, S. (2012). Basic econometrics. McGraw-Hill Higher Education

He, Y., & Zaslavsky, A. M. (2012). Diagnostics for multiple imputation in surveys with missing data. Biometrika, 99(4), 731-745.

Horowitz, J. L., and Markatou, M. (1996). Semiparametric estimation of regression models for panel data. Review of Economic Studies, 63(1), 145-168.

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2023). An Introduction to Statistical Learning. Publisher.

Lind, D. A., Marchal, W. G., and Wathen, S. A. (1967). Statistical Techniques in Business and Economics (2nd ed). Publisher

Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. Wiley.

Liu , R. Y. (1988). Bootstrap procedures under some non i.i.d. models . Annals of Statistics 16, 1696 – 1708

Politis, D. and Romano, J, (1994). The Stationary bootstap. The journal of American Statistical Association. 89 (428), 1303-1312

Schafer, J. L., & Graham, J. W. (2002). Multiple imputation for missing data: A cautionary tale. Sociological Methods & Research, 31(4), 445-454.

Yuklashlar

Nashr qilingan

Qanday qilib iqtibos keltirish kerak

Рахимов, З., & Рахимова, Н. (2024). МАЪЛУМОТЛАР ТАСОДИФИЙ ЙЎҚОЛГАН ЧИЗИҚЛИ РЕГРЕССИЯ: БООТСТРАП ЁНДАШУВИ. Iqtisodiy Taraqqiyot Va Tahlil, 2(4), 492–502. https://doi.org/10.60078/2992-877X-2024-vol2-iss4-pp492-502