Median polish

The median polish is a simple and robust exploratory data analysis procedure proposed by the statistician John Tukey. The purpose of median polish is to find an additively-fit model for data in a two-way layout table (usually, results from a factorial experiment) of the form row effect + column effect + overall median.

Median polish utilizes the medians obtained from the rows and the columns of a two-way table to iteratively calculate the row effect and column effect on the data. The results are not meant to be sensitive to the outliers, as the iterative procedure uses the medians rather than the means.

Model for a two-way table

Suppose an experiment observes the variable Y under the influence of two variables. We can arrange the data in a two-way table in which one variable is constant along the rows and the other variable constant along the columns. Let i and j denote the position of rows and columns (e.g. y_ij denotes the value of y at the ith row and the jth column). Then we can obtain a simple linear regression equation:

\mathbf {y} _{ij}=b_{0}+b_{1}x_{i}+b_{2}z_{j}+\varepsilon _{ij},

where $b 0, b 1, b 2$ are constants, and $x i$ and $z j$ are values associated with rows and columns, respectively.

The equation can be further simplified if no $x i$ and $z j$ values are present for the analysis:

\mathbf {y} _{ij}=b_{0}+c_{i}+d_{j}+\varepsilon _{ij},

where $c i$ and $d j$ denote row effects and column effects, respectively.

Procedure

To carry out median polish:

(1) find the row medians for each row, find the median of the row medians, record this as the overall effect.

(2) subtract each element in a row by its row median, do this for all rows.

(3) subtract the overall effect from each row median.

(4) do the same for each column, and add the overall effect from column operations to the overall effect generated from row operations.

(5) repeat (1)-(4) until negligible change occur with row or column medians

References

Frederick Mosteller and John Tukey (1977). "Data Analysis and Regression". Reading, MA: Addison-Wesley. ISBN 0-201-04854-X.
J.D. Emerson and D.C. Hoaglin (1983). "Analysis of two-way tables by medians". In "Understanding Robust and Exploratory Data Analysis", eds D. C. Hoaglin, F. Mosteller and J. W. Tukey. New York City: John Wiley & Sons. ISBN 0-471-38491-7. pp. 165–210.
William N. Venables and Brian D. Ripley (2002). Statistics Complements to Modern Applied Statistics with S, p.4–5. ISBN 0-387-95457-0.
Anwar Fitrianto, Hari Wijayanto, Sohel Rana, and Cheong Yee Voon (2014). "Median Polish for Final Grades of MTH3000- and MTH4000- Level Courses". Applied Mathematical Sciences, Vol. 8, no. 126, pp. 6295-6302

Statistics

Descriptive statistics

Continuous data

Center	Mean Arithmetic Arithmetic-Geometric Contraharmonic Cubic Generalized/power Geometric Harmonic Heronian Heinz Lehmer Median Mode
Dispersion	Average absolute deviation Coefficient of variation Interquartile range Percentile Range Standard deviation Variance
Shape	Central limit theorem Moments Kurtosis L-moments Skewness

Count data

Index of dispersion

Summary tables

Dependence

Graphics

Data collection

Study design	Effect size Missing data Optimal design Population Replication Sample size determination Statistic Statistical power
Survey methodology	Sampling Cluster Stratified Opinion poll Questionnaire Standard error
Controlled experiments	Blocking Factorial experiment Interaction Random assignment Randomized controlled trial Randomized experiment Scientific control
Adaptive designs	Adaptive clinical trial Stochastic approximation Up-and-down designs
Observational studies	Cohort study Cross-sectional study Natural experiment Quasi-experiment

Statistical inference

Statistical theory

Frequentist inference

Point estimation	Estimating equations Maximum likelihood Method of moments M-estimator Minimum distance Unbiased estimators Mean-unbiased minimum-variance Rao–Blackwellization Lehmann–Scheffé theorem Median unbiased Plug-in
Interval estimation	Confidence interval Pivot Likelihood interval Prediction interval Tolerance interval Resampling Bootstrap Jackknife
Testing hypotheses	1- & 2-tails Power Uniformly most powerful test Permutation test Randomization test Multiple comparisons
Parametric tests	Likelihood-ratio Score/Lagrange multiplier Wald

Specific tests

Z-test (normal) Student's t-test F-test
Goodness of fit	Chi-squared G-test Kolmogorov–Smirnov Anderson–Darling Lilliefors Jarque–Bera Normality (Shapiro–Wilk) Likelihood-ratio test Model selection Cross validation AIC BIC
Rank statistics	Sign Sample median Signed rank (Wilcoxon) Hodges–Lehmann estimator Rank sum (Mann–Whitney) Nonparametric anova 1-way (Kruskal–Wallis) 2-way (Friedman) Ordered alternative (Jonckheere–Terpstra) Van der Waerden test

Bayesian inference

Correlation	Pearson product-moment Partial correlation Confounding variable Coefficient of determination
Regression analysis	Errors and residuals Regression validation Mixed effects models Simultaneous equations models Multivariate adaptive regression splines (MARS)
Linear regression	Simple linear regression Ordinary least squares General linear model Bayesian regression
Non-standard predictors	Nonlinear regression Nonparametric Semiparametric Isotonic Robust Homoscedasticity and Heteroscedasticity
Generalized linear model	Exponential families Logistic (Bernoulli) / Binomial / Poisson regressions
Partition of variance	Analysis of variance (ANOVA, anova) Analysis of covariance Multivariate ANOVA Degrees of freedom

Categorical / Multivariate / Time-series / Survival analysis

Categorical

Multivariate

Time-series

General	Decomposition Trend Stationarity Seasonal adjustment Exponential smoothing Cointegration Structural break Granger causality
Specific tests	Dickey–Fuller Johansen Q-statistic (Ljung–Box) Durbin–Watson Breusch–Godfrey
Time domain	Autocorrelation (ACF) partial (PACF) Cross-correlation (XCF) ARMA model ARIMA model (Box–Jenkins) Autoregressive conditional heteroskedasticity (ARCH) Vector autoregression (VAR)
Frequency domain	Spectral density estimation Fourier analysis Least-squares spectral analysis Wavelet Whittle likelihood

Survival

Survival function	Kaplan–Meier estimator (product limit) Proportional hazards models Accelerated failure time (AFT) model First hitting time
Hazard function	Nelson–Aalen estimator
Test	Log-rank test

Applications

Biostatistics	Bioinformatics Clinical trials / studies Epidemiology Medical statistics
Engineering statistics	Chemometrics Methods engineering Probabilistic design Process / quality control Reliability System identification
Social statistics	Actuarial science Census Crime statistics Demography Econometrics Jurimetrics National accounts Official statistics Population statistics Psychometrics
Spatial statistics	Cartography Environmental statistics Geographic information system Geostatistics Kriging

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