
Composite likelihood estimation for a Gaussian process under fixed domain asymptotics
We study composite likelihood estimation of the covariance parameters wi...
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Handling Position Bias for Unbiased Learning to Rank in Hotels Search
Nowadays, search ranking and recommendation systems rely on a lot of dat...
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Modelling TimeVarying Rankings with Autoregressive and ScoreDriven Dynamics
We develop a new statistical model to analyse timevarying ranking data....
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Direct Estimation of Position Bias for Unbiased LearningtoRank without Intervention
The Unbiased LearningtoRank framework has been recently introduced as ...
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Performance and Application of Estimators for the Value of an Optimal Dynamic Treatment Rule
Given an (optimal) dynamic treatment rule, it may be of interest to eval...
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When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank
Besides position bias, which has been wellstudied, trust bias is anothe...
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On Estimating Many Means, Selection Bias, and the Bootstrap
With recent advances in high throughput technology, researchers often fi...
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Sequential Search Models: A Pairwise Maximum Rank Approach
This paper studies sequential search models that (1) incorporate unobserved product quality, which can be correlated with endogenous observable characteristics (such as price) and endogenous search cost variables (such as product rankings in online search intermediaries); and (2) do not require researchers to know the true distribution of the match value between consumers and products. A likelihood approach to estimate such models gives biased results. Therefore, I propose a new estimator – pairwise maximum rank (PMR) estimator – for both preference and search cost parameters. I show that the PMR estimator is consistent using only data on consumers' search order among one pair of products rather than data on consumers' full consideration set or final purchase. Additionally, we can use the PMR estimator to test for the true match value distribution in the data. In the empirical application, I apply the PMR estimator to quantify the effect of rankings in Expedia hotel search using two samples of the data set, to which consumers are randomly assigned. I find the position effect to be $0.11$0.36, and the effect estimated using the sample with randomly generated rankings is close to the effect estimated using the sample with endogenous rankings. Moreover, I find that the true match value distribution in the data is unlikely to be N(0,1). Likelihood estimation ignoring endogeneity gives an upward bias of at least $1.17; misspecification of match value distribution as N(0,1) gives an upward bias of at least $2.99.
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