- Research interests
- Statistical analysis of large datasets
- Dependent data (time series and networks)
- Efficient Monte Carlo methods
- Robust and nonparametric Bayesian methods
- Recent papers
- A Bayesian approach to the analysis of time symmetry
in light curves: Reconsidering Scorpius X-1 occultations.
Alexander Blocker, Pavlos Protopapas, & Charles Alcock. -
arXiv:0904.0645v1[astro-ph.IM]
- The True Cost of Social Security
Alexander W Blocker, Larry Kotlikoff, & Steve Ross
- Recent talks
- Two Problems in X-ray Astronomy
- Discussion of my work on two projects in x-ray astronomy: the development of a hierarchical Bayesian replacement for "stacking" and the analysis of events in x-ray light curves. For each problem, I outlined the development of an improved model for the data and the computational methods employed. I also discussd the unique challenges that each case has presented from a cultural perspective.
- Software
- bayesstack: Bayesian x-ray stacking analysis
- Part of the ChaMP software packages for the analysis of multiwavelength surveys
- Kalman tools for Matlab
- Kalman filter & smoother
- Allow for control inputs in state equation & affine term in measurement equation
- Maximum likelihood estimation of linear state-space systems
- Implementation of the expectation maximization algorithm
- Can estimate input matrix and/or affine term in measurement equation
- Optional diagonal restrictions on state & observation noise covariance matrices
- 12/06/2007: Updated with moderate efficiency improvements for M-step routines & major change in EM convergence criterion (relative instead of absolute change)
- 12/13/2007: Significant efficiency improvements and further tweaking of EM convergence criterion
- Licensed under LGPL v3.0
- A technical note on the EM algorithm for affine state-space systems & its usage
- Some useful scripts for R
- bagginglm.R: The beginning of a set of functions for bagging LMs and GLMs. Very preliminary. Licensed under GPL v2.0
- AICc.R: A function to calculate corrected AIC (AIC with an adjustment term for small-sample bias). This is written in the same way as the base AIC function, and will work for any model with a logLik method.
- split.data.R: A simple function to break apart a data frame or multivariate time series; it is particularly useful for dealing with the latter. Includes an option to omit missing values while splitting.
- Current Affiliations
- Researcher with Harvard University Statistics Department
- Currently working with astrostatistics group (CHASC) on X-ray stacking for ChaMP.
- Also working with the Time Series Center (part of the IIC) on computationally-intensive time series analysis.
- Teaching assistant with Harvard University Statistics Department
- Head TF for Statistics 104 with Professor Stanley for Spring 2009
- Background
- Boston University Alumnus, Class of 2008
- Bachelors in Mathematics & Economics
- Masters in Economics
- PhD-level coursework in statistics & econometrics
- Formerly:
- Teaching assistant for two sections of Stat 104 (Harvard, Fall 2008)
- Intern with Weiss Asset Management (June 2008 - June 2009)
- Research Assistant with Boston University Department of Economics
- Intern with UBS Fixed Income Research
- US Rates & Govt. Bonds Group
- Senior Research & IT Advisor, Matté & Company
- Research Assistant, Boston University School of Management
Contact: ablocker (at) gmail (dot) com