• Study Resource
  • Explore
    • Arts & Humanities
    • Business
    • Engineering & Technology
    • Foreign Language
    • History
    • Math
    • Science
    • Social Science

    Top subcategories

    • Advanced Math
    • Algebra
    • Basic Math
    • Calculus
    • Geometry
    • Linear Algebra
    • Pre-Algebra
    • Pre-Calculus
    • Statistics And Probability
    • Trigonometry
    • other →

    Top subcategories

    • Astronomy
    • Astrophysics
    • Biology
    • Chemistry
    • Earth Science
    • Environmental Science
    • Health Science
    • Physics
    • other →

    Top subcategories

    • Anthropology
    • Law
    • Political Science
    • Psychology
    • Sociology
    • other →

    Top subcategories

    • Accounting
    • Economics
    • Finance
    • Management
    • other →

    Top subcategories

    • Aerospace Engineering
    • Bioengineering
    • Chemical Engineering
    • Civil Engineering
    • Computer Science
    • Electrical Engineering
    • Industrial Engineering
    • Mechanical Engineering
    • Web Design
    • other →

    Top subcategories

    • Architecture
    • Communications
    • English
    • Gender Studies
    • Music
    • Performing Arts
    • Philosophy
    • Religious Studies
    • Writing
    • other →

    Top subcategories

    • Ancient History
    • European History
    • US History
    • World History
    • other →

    Top subcategories

    • Croatian
    • Czech
    • Finnish
    • Greek
    • Hindi
    • Japanese
    • Korean
    • Persian
    • Swedish
    • Turkish
    • other →
 
Profile Documents Logout
Upload
An Introduction to Bootstrap Methods with Applications to R
An Introduction to Bootstrap Methods with Applications to R

... including spatial data analysis, P-value adjustment in multiple testing, censored data, subset selection in regression models, process capability indices, and some new material on bioequivalence and covariate adjustment to area under the curve for receiver operating characteristics for diagnostic te ...
Springer Series in Statistics
Springer Series in Statistics

An introduction to Bootstrap Methods Outline Monte Carlo
An introduction to Bootstrap Methods Outline Monte Carlo

Statistics 1: MATH11400
Statistics 1: MATH11400

Extending Powell's Semiparametric Censored Estimator to Include Non-Linear Functional Forms and Extending Buchinsky's Estimation Technique
Extending Powell's Semiparametric Censored Estimator to Include Non-Linear Functional Forms and Extending Buchinsky's Estimation Technique

full version
full version

A Geometric Analysis of Subspace Clustering with Outliers
A Geometric Analysis of Subspace Clustering with Outliers

Linear Models in Econometrics
Linear Models in Econometrics

MEASURES OF DISPERSION :- 1. Dispersion refers to the variation
MEASURES OF DISPERSION :- 1. Dispersion refers to the variation

... (iii) It serves the basis of other statistical measures such as correlation etc. (iv) It serves the basis of statistical quality control. 3. Properties of a good measure of dispersion: (i) It should be easy to understand. (ii) It should be simple to calculate (iii) It should be uniquely defined. (iv ...
Analysis of variability of tropical Pacific sea surface temperatures
Analysis of variability of tropical Pacific sea surface temperatures

PDF
PDF

bagging
bagging

Bivariate censored regression relying on a new estimator of the joint
Bivariate censored regression relying on a new estimator of the joint

Applying bootstrap methods to time series and regression models
Applying bootstrap methods to time series and regression models

... Applying bootstrap methods to time series and regression models “An Introduction to the Bootstrap” by Efron and Tibshirani, chapters 8-9 M.Sc. Seminar in statistics, TAU, March 2017 By Yotam Haruvi ...
Estimation of the Information by an Adaptive Partitioning of the
Estimation of the Information by an Adaptive Partitioning of the

... Communicated by P. Moulin, Associate Editor for Nonparametric Estimation, Classification, and Neural Networks. Publisher Item Identifier S 0018-9448(99)03551-8. ...
Semiparametric regression analysis with missing response at ramdom
Semiparametric regression analysis with missing response at ramdom

... Introduction ...
Gerig, Thomas and Guillermo P. Zarate-De-Lara; (1976)Estimation in linear models using directionally minimax mean squared error."
Gerig, Thomas and Guillermo P. Zarate-De-Lara; (1976)Estimation in linear models using directionally minimax mean squared error."

Estimating Average Treatment Effects
Estimating Average Treatment Effects

Estimation of High-Dimensional Mean Regression in Absence of
Estimation of High-Dimensional Mean Regression in Absence of

Outliers - University of Notre Dame
Outliers - University of Notre Dame

... Descriptive statistics. It is always a good idea to start with descriptive statistics of your data. Besides the built-in command summarize, the user-written commands fre and extremes can be helpful here. (To save space I am only printing out a few of the frequencies.) ...
Notes 10
Notes 10

... Dispersion about the True Mean • For a comparison of the academic performance of this student with the rest of her graduating class, it is good to look at where they are ranked in the class but better to look at this in relation to the dispersion around the mean • How many standard deviations is a ...
Coefficient of Determination
Coefficient of Determination

STATISTICS WITH SPREADSHEETS What is a spreadsheet? A
STATISTICS WITH SPREADSHEETS What is a spreadsheet? A

1 CHECKING MODEL ASSUMPTIONS (CHAPTER 5) The
1 CHECKING MODEL ASSUMPTIONS (CHAPTER 5) The

... • Form a normal probability plot of residuals (Details shortly.) • Approximate normality is usually good enough for inferences concerning treatment means and contrasts to be reasonably good, especially if sample sizes are large (thanks to the CLT). • Heavy tails can be a problem -- non-parametric me ...
2004MinnP6.1
2004MinnP6.1

1 2 >

Robust statistics

Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal. Robust statistical methods have been developed for many common problems, such as estimating location, scale and regression parameters. One motivation is to produce statistical methods that are not unduly affected by outliers. Another motivation is to provide methods with good performance when there are small departures from parametric distributions. For example, robust methods work well for mixtures of two normal distributions with different standard-deviations; under this model, non-robust methods like a t-test work badly.
  • studyres.com © 2025
  • DMCA
  • Privacy
  • Terms
  • Report