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Weighted Wilcoxon-type Smoothly Clipped Absolute Deviation Method
Weighted Wilcoxon-type Smoothly Clipped Absolute Deviation Method

... leverage points. This often imposes serious problems for variable selection and the subsequent inference. Existing work on robust variable selection are mostly robust best-subset procedures, such as robust AIC or BIC, see Ronchetti (1985), Hurvich and Tsai (1990), Burman and Nolan (1995), Ronchetti ...
practice problems for exam 1
practice problems for exam 1

Giltinan, David M.Some New Estimation Methods for Weighted Regression When There Are Possible Outliers."
Giltinan, David M.Some New Estimation Methods for Weighted Regression When There Are Possible Outliers."

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A.A.M.Nurunnabi

Discriminant Diagnostics
Discriminant Diagnostics

Data Modeling and Least Squares Fitting 2 COS 323
Data Modeling and Least Squares Fitting 2 COS 323

Assessing the Uncertainty of Point Estimates We notice that, in
Assessing the Uncertainty of Point Estimates We notice that, in

... bias uncertainty cannot be simply reduced by increasing sample sizes. As illustrated before, naive increases of sample sizes could make a bad situation even worse! An obvious way for reducing bias uncertainty is to increase data quality, but this is usually very expensive when at all possible. An al ...
Estimating and Simulating a Model for Mean Reversion (in  format)
Estimating and Simulating a Model for Mean Reversion (in format)

E - NCSU Statistics
E - NCSU Statistics

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Exploratory Data Analysis

... with each value and its frequency of occurrence.  You can also compute a checking variable using the COMPUTE command. Create a new variable that gives a 1 if a value is between minimum and maximum, and a 0 if the value is outside that range.  Best way to examine categorical variables is by ...
AP-Test-Prep---Flashcards[2]
AP-Test-Prep---Flashcards[2]

point estimate
point estimate

... Some General Concepts of Point Estimation A sensible way to quantify the idea of being close to  is to consider the squared error ( )2. For some samples, will be quite close to  and the resulting squared error will be near 0. Other samples may give values of far from , corresponding to very larg ...
Chimiometrie 2009
Chimiometrie 2009

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Review of Part I

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... Use frequency density/area to construct a histogram Read information from a histogram and use it to calculate frequencies Construct and/or interpret a scatter diagram Construct and/or interpret a stem and leaf diagram (including back to back) Construct and/or interpret a box (and whisker)plot includ ...
Bivariate Data Cleaning
Bivariate Data Cleaning

... Applying group-specific outlier analysis • Do outlier analyses and trimming/Windsorizing separately for each group • This gets lengthy when working with factorial designs -- remember the purpose of each condition!!! • Some suggest taking an analogous approach with doing regression analyses that invo ...
Robust Estimation Problems in Computer Vision
Robust Estimation Problems in Computer Vision

RESEARCH LAB II (S3) Salem State College School of Social Work
RESEARCH LAB II (S3) Salem State College School of Social Work

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Week2

... • The statistical model corresponds to the information a statistician brings to the application about what the true distribution is or at least what he or she is willing to assume about it. • The variable θ is called the parameter of the model, and the set Ω is called the parameter space. ...
L20: Outliers
L20: Outliers

... already have a good model M , then why do we can about outliers? So here we discuss properties of techniques that build a model M and are resistant, or robust, to outliers. Given a model M (P ), its breakdown point is an upper bound of the fraction of points in P that can be moved to ∞ and for M (P ...
Martin & Zamar - Robust Statistics
Martin & Zamar - Robust Statistics

Exam I Review Math 1530: Elements of Statistics Type of variables
Exam I Review Math 1530: Elements of Statistics Type of variables

... 7. When data contains extreme observations (outliers), IQR can be a good measure of spread. 8. A z-score tells the direction and number of means a data value is from the standard deviation. 9. The median lies to the right of the mean for a right-skewed distribution. 10. The regression line has the p ...
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Formulas needed for EOCT Arithmetic Sequences Geometric

... To find the MAD find the mean of the third column. If you have a TI-36X Pro you can make this table on your calculator under L1, L2, and L3 and enter a formula under L3. Finding linear and exponential models of best fit (regression): Enter all of the x data under L1 and the y data under L2 in your c ...
Robust nonparametric statistical methods.
Robust nonparametric statistical methods.

b0 and b1 are unbiased (p. 42) Recall that least
b0 and b1 are unbiased (p. 42) Recall that least

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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.
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