Spatial regression models, used to model spatial relationships, have received considerable attention in recent years since numerous data sets have been collected with geographical references in space. The spatial error model (SEM), among spatial regression models, has been widely applied for spatial data in the literature due to its simple structure. However, it is known that the classical estimation methods such as the maximum likelihood and generalized moment can be influenced by the presence of outliers in the data. In this article, a robust estimation approach based on the robustified likelihood equations for SEM is proposed. The results of the simulation study show that the proposed estimator for SEM has smaller bias and mean squared errors and exhibits more robust empirical influence function than the classical methods, when there are outliers in the dataset. The results of all analysis show that the proposed estimator is robust to outliers.

This study is intended to examine the relationship between tourist attractions (natural, cultural and historical) and tourist flows. In the study, secondary data for six provinces and 110 sub-provinces in the Southwestern Anatolia region of Turkey, visited by local and foreign tourists, are used. Four of these provinces have a coastline to the Aegean Sea and the Mediterranean. In this context, overnight data of tourists for 110 sub-provinces and the printed and online materials and overnight data of tourists are used to identify attractions. In this study, mapping analysis, local and global Moran’s I, the classical regression and spatial regression models are benefited. Primarily, the spillover of attractions through maps and the distribution of tourist flows are presented in the study. When the relationship between tourist attractions and tourist flows are examined, the results of our analyses show that the Global Moran’s I value is 0.25 and that those 110 sub-provinces could be similar in terms of tourist flow. It was determined whether there is a global clustering based on Global Moran’s I value, and then the similar clusters, that is, similar sub-provinces in terms of tourist flow, were determined using the spectral clustering method. In addition, the neighborhood relationship and neighborhood interactions in terms of tourist flow are determined using local indicators of spatial analysis (LISA) alongside the Spectral Clustering Method. Finally, in the study field, the relationship between cultural, historical, and natural tourist attractions and tourist flow is explained using the classical regression model and the spatial regression model. The spatial-based models, especially the SEM, improve the model performance compared to the corresponding OLS model. In conclusion, it is found that there is a positive correlation between tourist flows and natural and historical attractions of the region, but a negative relationship between tourist flows and cultural attractions. Destination management implications are discussed.

It is well-known that classical Tobit estimator of the parameters of the censored regression (CR) model is inefficient in case of non-normal error terms. In this paper, we propose to use the modified maximum likelihood (MML) estimator under the Jones and Faddy's skew t-error distribution, which covers a wide range of skew and symmetric distributions, for the CR model. The MML estimators, providing an alternative to the Tobit estimator, are explicitly expressed and they are asymptotically equivalent to the maximum likelihood estimator. A simulation study is conducted to compare the efficiencies of the MML estimators with the classical estimators such as the ordinary least squares, Tobit, censored least absolute deviations and symmetrically trimmed least squares estimators. The results of the simulation study show that the MML estimators work well among the others with respect to the root mean square error criterion for the CR model. A real life example is also provided to show the suitability of the MML methodology.

In this study, parametric and semi-parametric regression models are examined for random right censorship. The components of the aforementioned regression models are estimated with weights based on Cox and Kaplan–Meier estimates, which are semi-parametric and nonparametric methods used in survival analysis, respectively. The Tobit based on weights obtained from a Cox regression is handled as a parametric model instead of other parametric models requiring distribution assumptions such as exponential, Weibull, and gamma distributions. Also, the semi-parametric smoothing spline and the semi-parametric smoothing kernel estimators based on Kaplan–Meier weights are used. Therefore, estimates are obtained from two models with flexible approaches. To show the flexible shape of the models depending on the weights, Monte Carlo simulations are conducted, and all results are presented and discussed. Two empirical datasets are used to show the performance of the aforementioned estimators. Although three approaches gave similar results to each other, the semi-parametric approach was slightly superior to the parametric approach. The parametric approach method, on the other hand, yields good results in medium and large sample sizes and at a high censorship level. All other findings have been shared and interpreted.

This qualitative study investigates the essential attributes required for successful rural tourism entrepreneurship. A series of focus group interviews with rural tourism professionals in Italy, Spain, and Türkiye was employed. The data collected identified five central themes: the attractive aspects of rural tourism entrepreneurship, the necessary competencies for a rural tourism entrepreneur, the training/support needed, barriers encountered, and the utilization of rural elements in tourism. The study finds that rural tourism provides numerous benefits, including economic and social growth, cultural exchange, and a close connection with nature. However, to harness these benefits, entrepreneurs need diverse skills, including knowledge of economics, tourism, legislation, and marketing. Additionally, they require specific training and support to navigate potential barriers like administrative procedures and high financial costs. The study's findings provide valuable insights for aspiring entrepreneurs, policymakers, and other stakeholders in the field. Despite its focus on three countries, it allows further research to incorporate a wider range of perspectives and geographical contexts.