Issa Zakeri's research areas include time series analysis, longitudinal data analysis, multivariate statistical analysis, sequential analysis, stochastic modeling and their applications to scientific problems. His primary area of applied research over the last six years has been in the application of statistical methods to behavioral, biological and medical sciences, in particular in the areas of 1) nutrimetrics—the application of statistical methods to problems in nutrition. The goal is to advance, develop and apply more accurate and computationally flexible statistical techniques to analyze and understand better many complex problems in nutrition, in particular behavioral nutrition; and 2) stochastic modeling and prediction of energy expenditure in children and adolescents. The research programs involve theoretical studies as well as computational methods in many branches of statistics, particularly analysis of high-dimensional and longitudinal data.
NIH 1R01 DK085163-01. Zakeri (PI on subcontract). 5/1/10- 4/30/14. Novel Approaches to Predict Energy Expenditure & Physical Activity Levels in Preschoolers.
NIH 1 R01 DK080909. Co-Investigator (PI: M. Lowe). 5/1/2009-4/31/2014.
A Test of Nutritional Interventions to Enhance Weight Loss Maintenance
NIH-AHRQ R21. Co-Investigator (PI: Sockolow). 09/30/2011-09/29/2013.
Impact of point-of-care Electronic Health Record in Home Care.
Butte N, Wong W, Adolph A, Puyau M, Vohra F, Zakeri I. Validation of Cross-sectional Time Series and Multivariate Adaptive Regression Splines Models for the Prediction of Energy Expenditure in Children and Adolescents using Doubly Labeled Water. The Journal of Nutrition 2010;104: 1516-1523.
Zakeri I, Adolph A, Puyau MR, Vohra FA, Butte N. Multivariate Adaptive Regression Splines (MARS) Models for the Prediction of Energy Expenditure in Children and Adolescents. Journal of Applied Physiology 2010; 108(1): 128-136.
Zakeri I, Adolph A, Puyau MR, Butte N. Prediction of Energy Expenditure from Heart Rate and Physical Activity in Children and Adolescents using Cross-Sectional Time Series Modeling. Journal of Applied Physiology 2008; 104(6): 1665-1673.
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