14 citations · 38 across the 7 of their papers we have counts for
7 papers
Price Optimization with Practical Constraints
Xiaojie Wang, Hsin-Chan Huang, Lanshan Han +1
In this paper, we study a retailer price optimization problem which includes the practical constraints: maximum number of price changes and minimum amount of price change (if a cha…
Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects
Hao Chen, Lanshan Han, Alvin Lim
Linear Mixed Effects (LME) models have been widely applied in clustered data analysis in many areas including marketing research, clinical trials, and biomedical studies. Inference…
Hierarchical Marketing Mix Models with Sign Constraints
Hao Chen, Minguang Zhang, Lanshan Han +1
Marketing mix models (MMMs) are statistical models for measuring the effectiveness of various marketing activities such as promotion, media advertisement, etc. In this research, we…
A Note on the Sum of Non-Identically Distributed Doubly Truncated Normal Distributions
Hao Chen, Lanshan Han, Alvin Lim
It is proved that the sum of n independent but non-identically distributed doubly truncated Normal distributions converges in distribution to a Normal distribution. It is also show…
Knot Locating in Piecewise Linear Approximation
Carlos Ugaz, Lanshan Han, Alvin Lim
Many separable nonlinear optimization problems can be approximated by their nonlinear objective functions with piecewise linear functions. A natural question arising from applying…
Quadratic Surface Support Vector Machine with L1 Norm Regularization
Ahmad Mousavi, Zheming Gao, Lanshan Han +1
We propose norm regularized quadratic surface support vector machine models for binary classification in supervised learning. We establish their desired theoretical proper…
Beyond the EM Algorithm: Constrained Optimization Methods for Latent Class Model
Hao Chen, Lanshan Han, Alvin Lim
Latent class model (LCM), which is a finite mixture of different categorical distributions, is one of the most widely used models in statistics and machine learning fields. Because…