Professor of Statistics


Department of Statistics

4103 Mathematical Sciences Building

University of California, Davis



Ph. D. in Statistics (1982), University of California, Berkeley

Master of Statistics (1977), Indian Statistical Institute, New Delhi, India

Bachelor of Statistics (1976), Indian Statistical Institute, Calcutta, India.


Department of Statistics, University of California, Davis, 1986- Present

Visiting Professor, National University of Singapore, 1998-2000

Department of Statistics, Rutgers University, New Brunswick, 1982-1986.


Research Interests:

Nonparametric function estimation, model fitting/selection, image analysis, time series, discrete data.



Winter Quarter 2016, Statistical Methods for Research (STA 207)

Spring Quarter 2016, Time Series Analysis (STA 137)

Spring Quarter 2016, Applied Statistics (STA 232C)


Editorial Boards:

Journal of Multivariate Analysis, 2012- 2015,

Statistics and Probability Letters, 2012- Present,

International Journal of Research, Rizvi College of Arts, Science & Commerce (current), 2012-Present.



NSF Research and Training Grant: “Statistics in the 21st Century - Objects, Geometry and Computing” (grant # DMS 1148643), Co-PI, 2012-2017.

NSF Research Grant: Dimensionality reduction methods in multivariate time series (grant # DMS 097622), PI, 2009-2014.

NSA Research Grant: Nonparametric and semiparametric methods in shape analysis (grant # H98230-04-1-0109), PI, 2004-2006.

NUS Research Grant: Model selection methods in Statistics, PI, 1998-2000.

NSF Research Grant: On some topics in nonparametrics (grant # DMS 9108295), PI, 1991-1994.


Burman, P. (1985). A data dependent approach to density estimation. Z. Wahr. Verw. Geb., 69, 609-628. 

Burman, P. (1987). Smoothing sparse contingency tables, Sankhya Ser A 49, 24-36

Burman, P. (1987). Central limit theorem for quadratic forms for sparse tables, Journal of Multivariate Analysis 22, 258-277. 

Burman, P. (1989). Rate of convergence of the spline estimates for Markov chains, Statistics & Probability Letters 8, 245-253. 

Burman, P. and K. W. Chen. (1989). Nonparametric estimation of a regression function, Annals of Statistics 17, 1567-1596. 

Burman, P. (1989). A comparative study of ordinary cross-validation, v-fold cross-validation and repeated learning testing methods, Biometrika 76, 503-514.

Burman, P. (1990). Estimation of generalized additive models, Journal of Multivariate Analysis 32, 230-255. 

Burman, P. (1990). Estimation of optimal transformations using v-fold cross validation and repeated learning-testing methods, Sankhya Ser A 52, 314-345. 

Burman, P. (1991). Rates of convergence for the estimates of the optimal transformations of variables, Annals of Statistics 19, 702-723. 

Burman, P. and D. Nolan. (1991). Some issues in cross-validation, in Nonparametric Functional Estimations and Related Topics, G. Roussas (ed.), Kluwer Publishers, 335, 603-612.

Burman, P. (1991). Regression function estimation from dependent observation, Journal of Multivariate Analysis 36, 263-279. 

Burman, P. and D. Nolan. (1992). Location-adaptive density estimation and nearest-neighbor distance, Journal of Multivariate Analysis 40, 132-157. 

Burman, P. and D. Nolan. (1992). Data dependent estimation of prediction functions, Journal of Time Series Analysis, 13, 198-208.

Burman, P., E. Chow and D. Nolan. (1994). A cross validatory method for dependent data, Biometrika 81, 351-358.

Burman, P. (1995). The fast monte-carlo cross-validation and CL procedures- comments, new results, and application to image recovery- Comment, Computational Statistics 10, 233-234. 

Burman, P. and D. Nolan. (1995). A general Akaike-type criterion for model selection in robust regression, Biometrika 82, 877-886. 

Burman, P. (1996). Model fitting via testing, Statistica Sinica 6, 589-601. 

Sacks, M., W. Silk and P. Burman. (1997). Effect of water stress on cortical cell division rates within the apical meristem of primary roots of maize, Plant Physiology 114, 519-527. 

Bhattacharya, P. K. and P. Burman. (1998). Semiparametric estimation in the multivariate Liouville models, Journal of Multivariate Analysis 65, 1-18. 

Burman, P. and R. H. Shumway. (1998). Semiparametric modeling of seasonal time series, Journal of Time Series Analysis 19, 127-145.

Grigione, M. M., P. Burman, V.C. Bleich et. al. (1999). Identifying individual mountain lions Felis concolor by their tracks: refinement of an innovative technique, Biological Conservation 88, 25-32. 

Grigione, M. M. and P. Burman. (2000). What is revealed in a mountain lion heel: using heel shape to ascertain identity, Transactions of the Western Chapter of Wildlife Scociety 36, 21-26. 

Sengupta, K., S. Wang, C.C. Ko and P. Burman. (2000). Automatic face modeling from monocular image sequences using modified non- parametric regression and an affine camera model, IEEE Proceedings on Automatic Face and Gesture Recognition 524–529. 

Zuruzi, A.S., K.S. Lahore, P. Burman, et. al. (2000). Correlation between intermetallic thickness and roughness during solder reflow, Journal of Electronic Materials 30, 997-1000. 

Sengupta, K., P. Burman, and S. Gupta. (2002). Least committed splines in 3D modeling of free form objects from intensity images, Journal of Mathematical Imaging and Vision 17, 175-186. 

Sengupta, K. and P. Burman. (2002). A curve fitting problem and its application in modeling objects in monocular image sequences, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 674-686. 

Burman, P. (2002). Estimation of equifrequency histograms, Statistics & Probability Letters 56, 227-238. 

Sengupta, K., P. Burman, and R. Sharma. (2004). A non-parametric approach for independent component analysis using kernel density estimation, IEEE Proceedings of Computer Vision and Pattern Recognition 27, 887-672. 

Burman, P. (2004). The estimation of prediction error: Covariance penalties and cross-validation  - Comment, Journal of American Statistical Association 99, 632-633.

Burman, P. (2004). On some testing problems for sparse contingency tables, Journal of Multivariate Analysis 88, 1-18. 

Richardson, A.D., B.H. Braswell, D.Y. Hollinger, et al. (2006). Comparing simple respiration models for eddy flux and dynamic chamber data, Agricultural and Forest Meteorology 141, 219-234. 

Hummel, N., F. Zalom, N. Toscano,  P. Burman, and C. Peng. (2006). Seasonal patterns of female Homalodisca coagulata (Say) (Hemiptera : Cicadellidae) reproductive physiology in Riverside, California, Environmental Entomology 35, 901-906. 

Burman, P. and R. H. Shumway. (2006). Generalized exponential predictors for time series forecasting, Journal of American Statistical Association 101, 1598-1606. 

Burman, P. (2006). Approximate singular values of the fractional difference and summation operators, Linear Algebra and Its Applications 416, 677-687. 

Burman, P. (2007). Sharp bounds for singular values of fractional integral operators, Journal of Mathematical Analysis and Applications. 327, 251-256.

Sengupta, K. and P. Burman. (2008). A non parametric approach for modeling interferometric SAR imagery and applications, IEEE Workshop on Applications of Computer Vision 1-6. 

Burman, and W. Polonik. (2009). Mode hunting in multi dimensions: data analytic tools with measures of significance, Journal of Multivariate Analysis 100, 1198-1218.

Burman, P. and R. H. Shumway. (2009). Estimation of trend in state-space models: asymptotic mean square error and rate of convergence, Annals of Statistics 37, 3715-3742. 

Njoroge S.M.C., G.E. Vallad, S.-Y. Park, S. Kang, S.T. Koike, M. Bolda, P. Burman, W. Polonik, and K.V. Subbarao. (2011). Phenological and phytochemical changes correlate with differential interactions of verticillium dahliae with broccoli and cauliflower. Phytopathology. 101(5), 523- 534. 

Grigione, M.M., P. Burman, S. Clavio, S. Harper, D. Manning, and R. Sarno. (2011). Diet of Florida coyotes in a protected wildland and suburban habitat. Urban Ecosystems 14, 655- 663.

Paul, D., J. Peng, and P. Burman. (2011). Semiparametric modeling of autonomous dynamical systems with applications to plant growth. Annals of Applied Statistics, 5(3): 2078-2018. 

Burman, P and P. Chaudhuri. (2011). On a hybrid approach to parametric and nonparametric regression. Nonparametric Statistical Methods and Related Topics: A Festschrift in Honor of Professor P K Bhattacharya on the Occasion of his 80th Birthday, ed. J. Jiang, F. Samaniego and G. G. Roussas, 233-256. World Scientific, Singapore. 

Hyun, J.W., P. Burman, D. Paul. (2012). A regression approach for estimating the parameters of the covariance function of a stationary random process. Journal of Statistical Planning and Inference, 142: 2300-2344. 

Das, S., N. Paul, A. Hazra, M. Ghosal, B. Kanti, T. K. Banerjee, P. Burman, S. K. Das. (2012). Cognitive Dysfunction in Stroke Survivors: A Community-Based Prospective Study from Kolkata, India. Journal of Stroke & Cerebrovascular Disease, 22: 1233-1242.

Grigione, M. M., P. Burman, S. Clavio, S. J. Harper, D. Manning, R. J. Sarno. (2013). comparative study between enteric parasites of coyotes in a protected and suburban habitat. Urban Ecosystems, 17: 1-10. 

Guha, P., K. Bhowmick, P. Mazumdar, M. Ghosal, I. Chakraborty, P. Burman. (2013). Assessment of Insulin Resistance and Metabolic Syndrome in Drug Naive Patients of Bipolar Disorders. Indian Journal of Clinical Biochemistry, 29: 51-56. 

Ghosal, M. K., P. Burman, V. Singh, S. Das, N. Paul, B. K. Ray, A. Hazra, T. K. Banerjee, A. Basu, A. Chaudhuri, S. K. Das. (2014). Correlates of Functional Outcome Among Stroke Survivors in a Developing Country - A Prospective Community-Based Study from India. Journal of Stroke & Cerebrovascular Disease, 23(10): 2614-2621. 

Noguchi, K., A. Aue and P. Burman. (2014). Exploratory Analysis and Modeling of Stock Returns. Journal of Computational and Graphical Statistics. IN PRESS.

Burman, P. (2014). A Hilbert-type inequality. Mathematical Inequalities & Applications. IN PRESS.

Paul, D., J. Peng and P. Burman. (2014). Nonparametric estimation of dynamics of monotone trajectories. To Appear, Annals of Statistics

Aue, A. and P. Burman. (2014) Estimation of Prediction Error in Time Series. IEEE Trans. Signal Processing (Submitted).

Burman, P. and D. Paul (2015).  Smooth predictive model fitting in regression. Journal of Multivariate Analysis (Submitted).