Semiparametric estimation of logistic regression model with missing covariates and outcome

We consider a semiparametric method to estimate logistic regression models with missing both covariates and an outcome variable, and propose two new estimators. The first, which is based solely on the validation set, is an extension of the validation likelihood estimator of Breslow and Cain (Biometrika 75:11–20, 1988). The second is a joint conditional likelihood estimator based on the validation and non-validation data sets. Both estimators are semiparametric as they do not require any model assumptions regarding the missing data mechanism nor the specification of the conditional distribution of the missing covariates given the observed covariates. The asymptotic distribution theory is developed under the assumption that all covariate variables are categorical. The finite-sample properties of the proposed estimators are investigated through simulation studies showing that the joint conditional likelihood estimator is the most efficient. A cable TV survey data set from Taiwan is used to illustrate the practical use of the proposed methodology.     

The effects of landscape fascination on subjective well-being and revisit intention: Evidence from agritourism destinations

This study adopted the reasonable person model (RPM) in the context of agritourism to identify tourists' preferences for features of agricultural landscapes; to examine the effects of agritourism landscape's fascination on subjective well-being and destination attachment, which in turn influence tourists' loyal behaviors; and to investigate the moderating effects of farm types. The quantitative and qualitative approaches were combined to obtain 1118 usable samples. The results validated the RPM by identifying the predictive power of landscape fascination, subjective well-being, and destination attachment on tourists' destination loyalty. Managerial implications for enhancing visitors' retentions in Taiwan were discussed.     

REDUCING RECOMMENDATION INEQUALITY VIA TWO-SIDED MATCHING: A FIELD EXPERIMENT OF ONLINE DATING

Leading dating platforms usually recommend only a small fraction of users based on users' popularity and similarity, leading to recommendation inequality. We use a stylized matching model from economics to modify existing algorithms to reduce inequality. We evaluate the proposed method through a large-scale field experiment on a dating platform. Experiment results suggest that our recommender reduces inequality, improves predictive accuracy, and leads to substantially more matched couples than other competing algorithms.     

Does office size matter in client acceptance decisions? Evidence from big 4 accounting firms

Review of Quantitative Finance and Accounting - This study examines whether audit firm office size affects auditors’ risk tolerance in making client acceptance decisions. Analyzing publicly...