[1] Aalen, O., Borgan, O., & Gjessing, H. (2008). Survival and event history analysis: a process point of view. New Yrok: Springer Science & Business Media.
[5] Barros, C. P., Correia, A., & Crouch, G. (2008). Determinants of the length of stay in Latin American tourism destinations. Tourism Analysis, 13(4), 329-340.
[9] Burger, C. J. S. C., Dohnal, M., Kathrada, M., & Law, R. (2001). A practitioners guide to time-series methods for tourism demand forecasting — a case study of Durban, South Africa. Tourism management, 22(4), 403-409.
[13] Dadgostar, B., & Isotalo, R. M. (1992). Factors affecting time spent by near-home tourists in city destinations. Journal of Travel Research, 31(2), 34-39.
[14] David, W., Hosmer, S., Stanley, L., & Susanne, M. (2008). Applied survival analysis: Regression Modeling of Time to Event Data (Second ed.). Hoboken: Wiley Interscience.
[16] De Menezes, A. G., Moniz, A., & Vieira, J. C. (2008). The determinants of length of stay of tourists in the Azores. Tourism Economics, 14(1), 205-222.
[17] Efron, B. (1977). The efficiency of Cox’s likelihood function for censored data. Journal of the American Statistical Association, 72(359), 557-565.
[18] Enger, A., Sandvik, K., & Kildal Iversen, E. (2015). Developing scenarios for the Norwegian travel industry 2025. Journal of Tourism Futures, 1(1), 6-18.
[19] Etzel, M. J., & Woodside, A. G. (1982). Segmenting vacation markets: The case of the distant and near-home travelers. Journal of Travel Research, 20(4), 10-14.
[20] Farr, M., & Guegan, X. (2013). The British Abroad Since the Eighteenth Century : travellers and tourists (Vol. 1& 2). Newcastle: Palgrave Macmillan.
[25] Gokovali, U., Bahar, O., & Kozak, M. (2007). Determinants of length of stay: A practical use of survival analysis. Tourism management, 28(3), 736-746.
[26] Goodall, B., & Ashworth, G. J. (1988). Marketing in the Tourism Industry: The promotion of destination regions. London: International Thomson Publishing Services.
[28] Harrell, F. E., Califf, R. M., Pryor, D. B., Lee, K. L., & Rosati, R. A. (1982). Evaluating the yield of medical tests. Journal of the American Medical Association, 247(18), 2543-2546.
[30] Hong, S.-k., & Jang, H. (2005). Factors influencing purchasing time of a new casino product and its managerial implications: An exploratory study. Journal of Travel Research, 43(4), 395-403.
[44] Nogawa, H., Yamaguchi, Y., & Hagi, Y. (1996). An empirical research study on Japanese sport tourism in sport-for-all events: Case studies of a single-night event and a multiplenight event. Journal of Travel Research, 35(2), 46-54.
[50] Song, H., Li, g., Witt, S. F., & Fei, B. (2010). Tourism demand modelling and forecasting: how should demand be measured. Tourism Economics, 16(1), 63-81.
[54] Thrane, C. (2012). Analyzing tourists’ length of stay at destinations with survival models: A constructive critique based on a case study. Tourism management, 33(1), 126-132.
[55] Thrane, C. (2015). Research note: The determinants of tourists’ length of stay: some further modelling issues. Tourism Economics, 21(5), 1087-1093.
[60] Turner, L. W., & Witt, S. F. (2001). Factors influencing demand for international tourism: Tourism demand analysis using structural equation modelling, revisited. Tourism Economics, 7(1), 21-38.
[61] Van den Berg, G. J. (2001). Chapter 55 - Duration models: specification, indetification and multiple durations. In J. J. Heckman & E. Leamer (Eds.), Handbook of econometrics (Vol. 5): Elsevier.
[63] Weaver, P. A., McCleary, K. W., Lapisto, L., & Damonte, L. T. (1994). The relationship of destination selection attributes to psychological, behavioral and demographic variables. Journal of Hospitality & Leisure Marketing, 2(2), 93-109.
[64] Witt, S. F., & Witt, C. A. (1995). Forecasting tourism demand: A review of empirical research. Interational journal of forecasting, 11(3), 447-475.