Jeff D. Hamann
Jeff is an econometrician interested in applying scientific methods to study, simulate, and optimize portfolios of financial and non-financial assets for long term stability, low volatility, and low maintenance. He has a Bachelors in Forestry from Humboldt State University, and an MS and PhD, in Forest Biometrics and Forest Engineering from Oregon State University. He has developed several software packages for econometrics and simulation for the R statistics project, and has written a book on Forest Analytics. He has been trading since college.
- Description: Professional Blogger. Trading frequency: Monthly
- Interests: Bonds, ETFs, Retirement savings
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Forest Analytics with R This book combines practical, down-to-earth data analysis and solutions to real management challenges with state-of-the-art statistical and data-handling functionality. With an emphasis in forestry, but useful for al types of capital intensive long term assets, the authors adopt a problem-driven approach, in ...More
which statistical and mathematical tools are introduced in the context of the analysis problem that they can help to resolve. All the tools are introduced in the context of real datasets, which provide compelling examples of practical applications. The modeling challenges covered within the book include imputation and interpolation for data, fitting probability density functions using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying and using models written in compiled languages, analysis of natural resources inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. It could also be used for readers familiar with geospatial and biological data to use many methods used in long-term asset management. The text assumes no programming background, some introductory statistics, and very basic applied mathematics.
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