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Challenges in Computational Statistics and Data Mining

Overview of attention for book
Cover of 'Challenges in Computational Statistics and Data Mining'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Evolutionary Computation for Real-World Problems
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    Chapter 2 Selection of Significant Features Using Monte Carlo Feature Selection
  4. Altmetric Badge
    Chapter 3 ADX Algorithm for Supervised Classification
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    Chapter 4 Estimation of Entropy from Subword Complexity
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    Chapter 5 Exact Rate of Convergence of Kernel-Based Classification Rule
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    Chapter 6 Compound Bipolar Queries: A Step Towards an Enhanced Human Consistency and Human Friendliness
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    Chapter 7 Process Inspection by Attributes Using Predicted Data
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    Chapter 8 Székely Regularization for Uplift Modeling
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    Chapter 9 Dominance-Based Rough Set Approach to Multiple Criteria Ranking with Sorting-Specific Preference Information
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    Chapter 10 On Things Not Seen
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    Chapter 11 Network Capacity Bound for Personalized Bipartite PageRank
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    Chapter 12 Dependence Factor as a Rule Evaluation Measure
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    Chapter 13 Recent Results on Nonparametric Quantile Estimation in a Simulation Model
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    Chapter 14 Adaptive Monte Carlo Maximum Likelihood
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    Chapter 15 What Do We Choose When We Err? Model Selection and Testing for Misspecified Logistic Regression Revisited
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    Chapter 16 Semiparametric Inference in Identification of Block-Oriented Systems
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    Chapter 17 Dealing with Data Difficulty Factors While Learning from Imbalanced Data
  19. Altmetric Badge
    Chapter 18 Personal Privacy Protection in Time of Big Data
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    Chapter 19 Data Based Modeling
Attention for Chapter 14: Adaptive Monte Carlo Maximum Likelihood
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

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3 X users
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1 Google+ user

Citations

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11 Dimensions

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5 Mendeley
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Chapter title
Adaptive Monte Carlo Maximum Likelihood
Chapter number 14
Book title
Challenges in Computational Statistics and Data Mining
Published in
arXiv, January 2016
DOI 10.1007/978-3-319-18781-5_14
Book ISBNs
978-3-31-918780-8, 978-3-31-918781-5
Authors

Błażej Miasojedow, Wojciech Niemiro, Jan Palczewski, Wojciech Rejchel, Blazej Miasojedow, Miasojedow, Błażej, Niemiro, Wojciech, Palczewski, Jan, Rejchel, Wojciech

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 20%
Unknown 4 80%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 2 40%
Professor 1 20%
Lecturer 1 20%
Unknown 1 20%
Readers by discipline Count As %
Mathematics 3 60%
Computer Science 1 20%
Unknown 1 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 January 2015.
All research outputs
#13,418,835
of 22,775,504 outputs
Outputs from arXiv
#228,537
of 935,406 outputs
Outputs of similar age
#189,373
of 393,343 outputs
Outputs of similar age from arXiv
#2,131
of 13,671 outputs
Altmetric has tracked 22,775,504 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 935,406 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 393,343 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 13,671 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.