J
John Liechty
Researcher at Pennsylvania State University
Publications - 59
Citations - 2979
John Liechty is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Bayesian probability & Markov chain Monte Carlo. The author has an hindex of 22, co-authored 57 publications receiving 2792 citations. Previous affiliations of John Liechty include Carnegie Mellon University & College of Business Administration.
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Modeling Online Browsing and Path Analysis Using Clickstream Data
TL;DR: This work shows how path information can be categorized and modeled using a dynamic multinomial probit model of Web browsing and finds that purchasers can be predicted with more than 40% accuracy, which is much better than the benchmark 7% purchase conversion prediction rate made without path information.
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Portfolio Selection with Higher Moments
TL;DR: This work proposes a method for optimal portfolio selection using a Bayesian decision theoretic framework that addresses two major shortcomings of the traditional Markowitz approach: the ability to handle higher moments and parameter uncertainty, and employs the skew normal distribution.
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Portfolio selection with higher moments
TL;DR: In this article, a Bayesian decision theoretic framework was proposed for optimal portfolio selection using a skew normal distribution, which has many attractive features for modeling multivariate returns. But, it is important to incorporate higher order moments in portfolio selection, which leads to higher expected utility than the traditional Markowitz approach.
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Incentive-Aligned Conjoint Analysis
TL;DR: This paper conducted a field experiment in a Chinese restaurant during dinnertime and found that the incentive-aligned choice conjoint outperforms the hypothetical choice conjooint analysis in terms of predictive performance.