Dr. Peter Adamic received his bachelor's degree in Actuarial Science from the University of Waterloo. After a stint working in the actuarial field, Dr. Adamic returned to academia, completing a master's degree and a doctorate in Statistics at the University of Guelph. A few months after receiving his doctorate, Dr. Adamic was hired as a tenure track professor at Laurentian University and was awarded tenure in 2012. After 14 years working at Laurentian, in 2023, Dr. Adamic accepted a position as Associate Professor of Mathematics at Ave Maria University in Florida, U.S.A.
Dr. Adamic's notable scholarship has appeared in reputable journals especially within the actuarial research ambit, including the Scandinavian Actuarial Journal, the Annals of Actuarial Science, and the European Actuarial Journal. In addition, Dr. Adamic has also published papers in more statistically-oriented journals, including Metrika, Statistical Inference for Stochastic Processes, the Journal of Statistical and Econometric Methods, and Model Assisted Statistics and Applications.
Dr. Adamic has secured several research grants, most notably two with the Natural Sciences and Engineering Research Council of Canada (NSERC).
Dr. Adamic has supervised many Master's students and has supervised two post-doctoral fellows. Dr. Adamic has refereed articles submitted to the following journals: Lifetime Data Analysis, the Journal of Statistical Computation and Simulation, Perceptual and Motor Skills, the Journal of Applied Statistics, Model Assisted Statistics and Applications, Risks, and Entropy.
Dr. Adamic is an Associate of the Canadian Institute of Actuaries (ACIA) and is also accredited as a Professional Statistician with the Statistical Society of Canada (P.Stat.).
Dr. Peter Adamic received his bachelor's degree in Actuarial Science from the University of Waterloo. After a stint working in the actuarial field, Dr. Adamic returned to academia, completing a master's degree and a doctorate in Statistics at the University of Guelph. A few months after receiving his doctorate, Dr. Adamic was hired as a tenure track professor at Laurentian University and was awarded tenure in 2012. After 14 years working at Laurentian, in 2023, Dr. Adamic accepted a position as Associate Professor of Mathematics at Ave Maria University in Florida, U.S.A.
Dr. Adamic's notable scholarship has appeared in reputable journals especially within the actuarial research ambit, including the Scandinavian Actuarial Journal, the Annals of Actuarial Science, and the European Actuarial Journal. In addition, Dr. Adamic has also published papers in more statistically-oriented journals, including Metrika, Statistical Inference for Stochastic Processes, the Journal of Statistical and Econometric Methods, and Model Assisted Statistics and Applications. For a more comprehensive list of publications, please refer to the publications tab.
Dr. Adamic has secured several research grants, most notably two with the Natural Sciences and Engineering Research Council of Canada (NSERC).
Dr. Adamic has supervised many Master's students and has supervised two post-doctoral fellows. Dr. Adamic has refereed articles submitted to the following journals: Lifetime Data Analysis, the Journal of Statistical Computation and Simulation, Perceptual and Motor Skills, the Journal of Applied Statistics, Model Assisted Statistics and Applications, Risks, and Entropy.
Dr. Adamic is an Associate of the Canadian Institute of Actuaries (ACIA) and is also accredited as a Professional Statistician with the Statistical Society of Canada (P.Stat.).
Chen, T., Desmond, A.F., Adamic, P. (2023), Journal of Statistical and Econometric Methods, 12(4),1-37.
This paper examines the problem of accurately estimating the expected value and variance of aggregate claims for each policyholder. Through an appropriate statistical model to estimate the pure premium, an insurer can find niche markets to operate competitively and profitably.
Beknazaryan, A., Sang, H., Adamic, P. (2023), Statistical Inference for Stochastic Processes, 26, 235-254.
Considered wavelets have compact support and are twice continuously differentiable. The number of vanishing moments of mother wavelet is proportional to the number of nonzero coefficients in the linear process and to the rate of decay of characteristic function of innovations. Theoretical results are illustrated by simulation studies with innovations following Gaussian, Cauchy and chi-squared distributions.
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