Mahsa Ashouri
Mahsa Ashouri

Assistant Professor

Department of Statistics, Miami University
Interests
  • Time series forecasting and time series clustering
  • Data mining, predictive analytics and business analytics
  • Big data analysis
  • Survival analysis
Education

  • PhD in Statistics - Service Science

    National Tsing Hua University, Taiwan

  • M.Sc. in Statistics

    Isfahan University of Technology, Iran

  • B.Sc. in Statistics

    Isfahan University of Technology, Iran

Featured Publications

Generalized Bayesian additive regression trees for restricted mean survival time inference

Prediction methods for time-to-event outcomes often utilize survival models that rely on strong assumptions about noninformative censoring or on how individual-level covariates and survival functions are related. When the main interest is in predicting individual-level restricted mean survival times (RMST), reliance on such assumptions can lead to poor predictive performance if these assumptions are not satisfied. We propose a generalized Bayes framework that avoids full probability modeling of all survival outcomes by using an RMST-targeted loss function that depends on a collection of inverse probability of censoring weights (IPCW). In our generalized Bayes formulation, we utilize a flexible additive tree regression model for the RMST function, and the posterior distribution of interest is obtained through model-averaging IPCW-conditional loss function-based pseudo-Bayesian posteriors. Because informative censoring can be captured by the IPCW-dependent loss function, our approach only requires one to specify a model for the censoring distribution, thereby obviating the need for complex joint modeling to handle informative censoring. We evaluate the performance of our method through a series of simulations that compare it with several well-known survival machine learning methods, and we illustrate the application of our method using a multi-site cohort of breast cancer patients with clinical and genomic covariates.

Fast forecast reconciliation using linear models

Forecasting hierarchical or grouped time series usually involves two steps, computing base forecasts and reconciling the forecasts. Base forecasts can be computed by popular time series forecasting methods such as Exponential Smoothing (ETS) and Autoregressive Integrated Moving Average (ARIMA) models. The reconciliation step is a linear process that adjusts the base forecasts to ensure they are coherent. However using ETS or ARIMA for base forecasts can be computationally challenging when there are a large number of series to forecast, as each model must be numerically optimized for each series. We propose a linear model that avoids this computational problem and handles the forecasting and reconciliation in a single step. The proposed method is very flexible in incorporating external data, handling missing values and model selection. We illustrate our approach using two datasets; monthly Australian domestic tourism and daily Wikipedia pageviews. We compare our approach to reconciliation using ETS and ARIMA, and show that our approach is much faster while providing similar levels of forecast accuracy.

Recent Publications
Mahsa Ashouri, Nicholas C. Henderson (2024). Generalized Bayesian additive regression trees for restricted mean survival time inference.
PDF Cite Code Dataset Source Document
Mahsa Ashouri, Frederick Kin Hing Phoa, Marzia A. Cremona (2023). Analyzing Taiwanese traffic patterns on consecutive holidays through forecast reconciliation and prediction-based anomaly detection techniques.
PDF Cite Code Dataset Source Document
Mahsa Ashouri, Frederick Kin Hing Phoa, Chun-Houh Chen, Galit Shmueli (2023). An interactive clustering-based visualization tool for air quality data analysis. Aerosol and Air Quality Research.
PDF Cite Code Dataset Source Document
Mahsa Ashouri, Frederick Kin Hing Phoa (2022). Interactive tool for clustering and forecasting patterns of Taiwan COVID-19 spread. PLOS ONE.
Cite Code Dataset Source Document
Hohyun Jung, Frederick Kin Hing Phoa, Mahsa Ashouri (2022). A leading author model for the popularity effect on scientific collaboration. Complex Networks.
Cite Source Document
See all publications