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Dealing with missing data based on data envelopment analysis and halo effect. (English) Zbl 1426.90171
Summary: This research attempts to solve the problem of dealing with missing data via the interface of Data Envelopment Analysis (DEA) and human behavior. Missing data is under continuing discussion in various research fields, especially those highly dependent on data. In practice and research, some necessary data may not be obtained in many cases, for example, procedural factors, lack of needed responses, etc. Thus the question of how to deal with missing data is raised. In this paper, modified DEA models are developed to estimate the appropriate value of missing data in its interval, based on DEA and Inter-dimensional Similarity Halo Effect. The estimated value of missing data is determined by the General Impression of original DEA efficiency. To evaluate the effectiveness of this method, the impact factor is proposed. In addition, the advantages of the proposed approach are illustrated in comparison with previous methods.

90B50 Management decision making, including multiple objectives
90C70 Fuzzy and other nonstochastic uncertainty mathematical programming
Full Text: DOI
[1] Tsikriktsis, N., A review of techniques for treating missing data in OM survey research, J. Oper. Manage., 24, 53-62, (2005)
[2] T. Kuosmanen, Modeling black data entries in Data Envelopment Analysis, Working Paper, Wageningen University, Netherlands, 2002. · Zbl 1139.90380
[3] Wang, S., Classification with incomplete survey data: a Hopfield neural network approach, Comput. Oper. Res., 32, 2583-2594, (2005) · Zbl 1073.62084
[4] Kros, J. F.; Lin, M.; Brown, M. L., Effects of the neural network s-Sigmoid function on KDD in the presence of imprecise data, Comput. Oper. Res., 33, 3136-3149, (2006) · Zbl 1113.90073
[5] Wang, H.; Wang, S., Towards optimal use of incomplete classification data, Comput. Oper. Res., 36, 1221-1230, (2009) · Zbl 1160.90717
[6] Austin, P. C.; Escobar, M. D., Bayesian modeling of missing data in clinical research, Comput. Stat. Data Anal., 49, 821-836, (2005) · Zbl 1429.62488
[7] Mojirsheibani, M., Nonparametric curve estimation with missing data: A general empirical process approach, J. Stat. Planin. Inference., 137, 2733-2758, (2007) · Zbl 1331.62221
[8] Stoica, P.; Xu, L. Z.; Li, J., A new type of parameter estimation algorithm for missing data problems, Stat. Probab. Lett., 75, 219-229, (2005) · Zbl 1085.62023
[9] Roth, P. L.; Switzer, F. S., A Monte Carlo analysis of missing data techniques in a HRM setting, J. Manage., 21, 1003-1023, (1995)
[10] Dawes, J., Do data characteristics change according to the number of scale points used?, Intern. J. Mark. Res., 50, 61-77, (2008)
[11] Singh, J.; Howell, R. D.; Rhoads, G. K., Adaptive designs for likert-type data: an approach for implementing marketing surveys, J. Mark. Res., 27, 304-321, (1990)
[12] Quinten, A.; Raaijmakers, W., Effectiveness of different missing data treatments in surveys with liker-type data: introducing the relative mean substitution approach, Educ. Psychol. Meas., 59, 725-748, (1999)
[13] Little, R. J.A.; Rubin, D. B., Statistical analysis with missing data, (2002), Wiley Canada · Zbl 1011.62004
[14] Olinsky, A.; Chen, S.; Harlow, L., The comparative efficacy of imputation methods for missing data in structural equation modeling, Eur. J. Oper. Res., 151, 53-79, (2003) · Zbl 1113.62361
[15] Nakagawa, S.; Freckleton, R. P., Missing inaction: the dangers of ignoring missing data, Trend. Ecol. Evol., 23, 11, 592-596, (2008)
[16] Schafer, J. L.; Graham, J. M., Missing data: our view of the state of the art, Psychol. Meth., 7, 147-177, (2002)
[17] C. Kao, S.T. Liu, Data envelopment analysis with missing data: A reliable solution method, in: J. Zhu, W.D. Cook (Eds.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, Springer, US, 2007, pp. 291-304. · Zbl 1171.90432
[18] Smirlis, Y. G.; Maragos, E. K.; Despotis, D. K., Data envelopment analysis with missing values: an interval DEA approach, Appl. Math. Comput., 177, 1-10, (2006) · Zbl 1152.62301
[19] Despotis, D. K.; Smirlis, Y. G., Data envelopment analysis with imprecise data, Eur. J. Oper. Res., 140, 24-36, (2002) · Zbl 1030.90055
[20] Entani, T.; Maeda, Y.; Tanaka, H., Dual models of interval DEA and its extension to interval data, Eur. J. Oper. Res., 136, 32-45, (2002) · Zbl 1087.90513
[21] Cooper, W. W.; Park, K. S.; Yu, G., IDEA and AR-IDEA: models for dealing with imprecise data in DEA, Manage. Sci., 45, 4, 597-607, (1999) · Zbl 1231.90289
[22] Wen, M.; Li, H., Fuzzy data envelopment analysis (DEA): model and ranking method, J. Comput. Appl. Math., 223, 2, 872-878, (2009) · Zbl 1159.90533
[23] Zhou, Z.; Yang, W.; Ma, C.; Liu, W., A comment on “A comment on ‘A fuzzy DEA/AR approach to the selection of flexible manufacturing systems’“ and “A fuzzy DEA/AR approach to the selection of flexible manufacturing systems”, Comput. Ind. Eng., 59, 4, 1019-1021, (2010)
[24] Jahanshahloo, G. R.; Damaneh, M. S.; Nasrabadi, E., Measure of efficiency in DEA with fuzzy input-output levels: a methodology for assessing, ranking and imposing of weights restrictions, Appl. Math. Comput., 156, 175-187, (2004) · Zbl 1134.90444
[25] Wu, D. D., Performance evaluation: an integrated method using data envelopment analysis and fuzzy preference relations, Eur. J. Oper. Res., 194, 1, 227-235, (2009) · Zbl 1158.90005
[26] Sylvia, L. T.; Till, B. D.; Swaminathan, S., Product attribute valuation: understanding the role of consumer experience and halo effects, Adv. Consum. Res., 33, 1, 678-679, (2006)
[27] Gilbride, T. J.; Yang, S.; Allenby, G. M., Modeling simultaneity in survey data, Quant. Mark. Econ., 3, 311-335, (2005)
[28] Wirtz, J., An examination of the presence, magnitude and impact of halo on consumes satisfaction measures, J. Retail. Consum. Serv., 7, 2, 89-99, (2000)
[29] Palmer, J. K.; Feldman, J. M., Accountability and need for cognition effects on contrast, halo, and accuracy in performance ratings, J. Psychol., 136, 2, 119-137, (2005)
[30] Palmer, J. K.; Loveland, J. M., The influence of group discussion on performance judgments: rating accuracy, contrast effects, and halo, J. Psychol., 142, 2, 117-130, (2008)
[31] Palmer, J.; Thomas, A.; Maurer, T., Moderating effects of context on the relationship between behavioral diaries and performance rating halo and accuracy, N. Am. J. Psychol., 5, 1, 81-90, (2003)
[32] Boatwright, P.; Kalra, A.; Zhang, W., Research note: should consumers use the halo to form product evaluations, Manage. Sci., 54, 1, 217-223, (2008)
[33] Kao, C.; Yang, Y. C., Reorganization of forest districts via efficiency measurement, Eur. J. Oper. Res., 58, 3, 356-362, (1992)
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