Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Hospitality Management. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Hospitality Management, 76, Part A, 2018 DOI: 10.1016/j.ijhm.2018.04.002
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Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Non-parametric regression for hypothesis testing in hospitality and tourism research
AU - Assaf, A. George
AU - Tsionas, Mike
N1 - This is the author’s version of a work that was accepted for publication in International Journal of Hospitality Management. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Hospitality Management, 76, Part A, 2018 DOI: 10.1016/j.ijhm.2018.04.002
PY - 2019/1
Y1 - 2019/1
N2 - The goal of this paper is to promote the use of Non-Parametric Regression (NPR) for hypothesis testing in hospitality and tourism research. In contrast to linear regression models, NPR frees researchers from the need to impose a priori specification on functional forms, thus allowing more flexibility and less vulnerability to misspecification problems. Importantly, we discuss in this paper a Bayesian approach to NPR using a Gaussian Process Prior (GPP). We illustrate the advantages of this method using an interesting application on internationalization and hotel performance. Specifically, we show how in contrast to linear regression, NPR decreases the risk of making incorrect hypothesis statements by revealing the true and full relationship between the variables of interest.
AB - The goal of this paper is to promote the use of Non-Parametric Regression (NPR) for hypothesis testing in hospitality and tourism research. In contrast to linear regression models, NPR frees researchers from the need to impose a priori specification on functional forms, thus allowing more flexibility and less vulnerability to misspecification problems. Importantly, we discuss in this paper a Bayesian approach to NPR using a Gaussian Process Prior (GPP). We illustrate the advantages of this method using an interesting application on internationalization and hotel performance. Specifically, we show how in contrast to linear regression, NPR decreases the risk of making incorrect hypothesis statements by revealing the true and full relationship between the variables of interest.
KW - Bayesian
KW - GPP
KW - Non-Parametric Regression
U2 - 10.1016/j.ijhm.2018.04.002
DO - 10.1016/j.ijhm.2018.04.002
M3 - Journal article
VL - 76
SP - 43
EP - 47
JO - International Journal of Hospitality Management
JF - International Journal of Hospitality Management
SN - 0278-4319
IS - Part A
ER -