Power of Ordered Hypothesis Testing
Authors: Lihua Lei, William Fithian
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare these methods using the GEOQuery data set analyzed by (Li & Barber, 2015) and find Adaptive Seq Step has favorable performance for both good and bad prior orderings. . . . In Section 5, we discuss selection of parameters and evaluate the finite-sample performance by simulation. In Section 6, we re-analyze the dosage response data from Li & Barber (2015), illustrating the predictions of our theory in real data. |
| Researcher Affiliation | Academia | Lihua Lei LIHUA.LEI@BERKELEY.EDU Department of Statistics, University of California, Berkeley William Fithian WFITHIAN@BERKELEY.EDU Department of Statistics, University of California, Berkeley |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper states: "Where possible we have re-used the R code provided by Li & Barber (2015) at their website." However, it does not state that the authors are providing open-source code for their own proposed methodology (AS/SS). |
| Open Datasets | Yes | Li & Barber (2015) analyzed the performance of several ordered testing methods using the GEOquery data of Davis & Meltzer (2007). |
| Dataset Splits | No | The paper discusses data analysis and simulation but does not provide specific details on training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU, memory, or cloud instance types) used for running the experiments or simulations. |
| Software Dependencies | No | The paper mentions "R code" but does not specify any software dependencies with version numbers (e.g., specific R packages or versions). |
| Experiment Setup | Yes | We set q = 0.1, γ = 0.2, µ = 2, b = 3.65, in which case Π(0) = 0.75, Π(1) = 0.2. Each panel shows power for n = 100, 500, 1000, and 10, 000. For each setting we simulate 500 realizations... |