credsubs: Multiplicity-Adjusted Subset Identification
نویسندگان
چکیده
منابع مشابه
A new nonparametric approach for multiplicity control: Optimal Subset procedures
A new approach for multiplicity control (Optimal Subset) is presented. It is based on the selection of the best subset of partial (univariate) hypotheses which, when combined, produce the minimal p-value. The second step consists in multiplicity adjustment. In this work we show how to perform this new procedure in the permutation framework, choosing adequate combining functions and permutation ...
متن کاملEKF–GPR-Based Fingerprint Renovation for Subset-Based Indoor Localization with Adjusted Cosine Similarity
Received Signal Strength Indicator (RSSI) localization using fingerprint has become a prevailing approach for indoor localization. However, the fingerprint-collecting work is repetitive and time-consuming. After the original fingerprint radio map is built, it is laborious to upgrade the radio map. In this paper, we describe a Fingerprint Renovation System (FRS) based on crowdsourcing, which avo...
متن کاملSynsetRank: Degree-adjusted Random Walk for Relation Identification
In relation extraction, a key process is to obtain good detectors that find relevant sentences describing the target relation. To minimize the necessity of labeled data for refining detectors, previous work successfully made use of BabelNet, a semantic graph structure expressing relationships between synsets, as side information or prior knowledge. The goal of this paper is to enhance the use o...
متن کاملIdentification of a Highly Mobilizable Subset of Human Neutrophil Intracellular
Tetranectin, a protein recently identified in a wide variety of human secretory cells (Christensen, L., and I. Clemmensen. 1989. Histochemistry. 92:29-35) was found to colocalize with latent alkaline phosphatase activity in fractions well separated from azurophil granules, specific granules, gelatinase-containing granules, and plasma membranes when postnuclear supernatants of nitrogen-cavitated...
متن کاملFeature Subset Selection and Order Identification for Unsupervised Learning
This paper explores the problem of feature subset selection for unsupervised learning within the wrapper framework. In particular, we examine feature subset selection wrapped around expectation-maximization (EM) clustering with order identiication (identifying the number of clusters in the data). We investigate two diierent performance criteria for evaluating candidate feature subsets: scatter ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Statistical Software
سال: 2020
ISSN: 1548-7660
DOI: 10.18637/jss.v094.i07