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Full-Text Articles in Statistics and Probability
Statistical Inference For Data Adaptive Target Parameters, Mark J. Van Der Laan, Alan E. Hubbard, Sara Kherad Pajouh
Statistical Inference For Data Adaptive Target Parameters, Mark J. Van Der Laan, Alan E. Hubbard, Sara Kherad Pajouh
U.C. Berkeley Division of Biostatistics Working Paper Series
Consider one observes n i.i.d. copies of a random variable with a probability distribution that is known to be an element of a particular statistical model. In order to define our statistical target we partition the sample in V equal size sub-samples, and use this partitioning to define V splits in estimation-sample (one of the V subsamples) and corresponding complementary parameter-generating sample that is used to generate a target parameter. For each of the V parameter-generating samples, we apply an algorithm that maps the sample in a target parameter mapping which represent the statistical target parameter generated by that parameter-generating …
The X-Alter Algorithm: A Parameter-Free Method Of Unsupervised Clustering, Thomas Laloë, Rémi Servien
The X-Alter Algorithm: A Parameter-Free Method Of Unsupervised Clustering, Thomas Laloë, Rémi Servien
Journal of Modern Applied Statistical Methods
Using quantization techniques, Laloë (2010) defined a new clustering algorithm called Alter. This L1-based algorithm is shown to be convergent but suffers two major flaws. The number of clusters, K, must be supplied by the user and the computational cost is high. This article adapts the X-means algorithm (Pelleg & Moore, 2000) to solve both problems.
Constructing A More Powerful Test In Two-Level Block Randomized Designs, Spyros Konstantopoulos
Constructing A More Powerful Test In Two-Level Block Randomized Designs, Spyros Konstantopoulos
Journal of Modern Applied Statistical Methods
A more powerful test is proposed for the treatment effect in two-level block randomized designs where random assignment takes place at the first level. When clustering at the second level is assumed to be known, the proposed test produces higher estimates of power than the typical test.