A Wavelet is a mathematical function used to write down a function or signal in terms of other functions that are simpler to study. Many signal processing tasks can be seen in terms of a wavelet transform. Informally speaking, the signal can be seen under the lens with a magnification given by the scale of the wavelet. In doing so, we can see only the information that is determined by the shape of the wavelet used.
The English term "wavelet" was introduced in the early 1980s by French physicists Jean Morlet and Alex Grossman.
They used the French word "ondelette" (which means "small wave").
Later, this word was brought into English by translating "onde" into "wave" giving "wavelet".
Wavelet is (complex) function from the Hilbert space
. For practical applications it should satisfy following conditions.
It must have finite energy.

It must satisfy an admissibility condition.
, where
is a Fourier transform of 
Zero mean condition implies from admissibility condition.

The function
is called mother wavelet. Its translated (shifted) and dilated (scaled) normalized versions are defined as following.

Original mother wavelet has parameters
and
. Translation is described by
parameter and dilatation by
parameter.
|
|---|
|
|
|---|
| Continuous data | |
|---|
| Count data | |
|---|
| Summary tables | |
|---|
| Dependence | |
|---|
| Graphics |
- Bar chart
- Biplot
- Box plot
- Control chart
- Correlogram
- Fan chart
- Forest plot
- Histogram
- Pie chart
- Q–Q plot
- Run chart
- Scatter plot
- Stem-and-leaf display
- Radar chart
- Violin plot
|
|---|
|
|
|
|---|
| Study design |
- Population
- Statistic
- Effect size
- Statistical power
- Optimal design
- Sample size determination
- Replication
- Missing data
|
|---|
| Survey methodology | |
|---|
| Controlled experiments | |
|---|
| Adaptive Designs |
- Adaptive clinical trial
- Up-and-Down Designs
- Stochastic approximation
|
|---|
| Observational Studies |
- Cross-sectional study
- Cohort study
- Natural experiment
- Quasi-experiment
|
|---|
|
|
|
|---|
| Statistical theory | |
|---|
| Frequentist inference | | Point estimation |
- Estimating equations
- Unbiased estimators
- Mean-unbiased minimum-variance
- Rao–Blackwellization
- Lehmann–Scheffé theorem
- Median unbiased
- Plug-in
|
|---|
| Interval estimation | |
|---|
| Testing hypotheses |
- 1- & 2-tails
- Power
- Uniformly most powerful test
- Permutation test
- Multiple comparisons
|
|---|
| Parametric tests |
- Likelihood-ratio
- Score/Lagrange multiplier
- Wald
|
|---|
|
|---|
| Specific tests | | | Goodness of fit | |
|---|
| Rank statistics |
- Sign
- Signed rank (Wilcoxon)
- Rank sum (Mann–Whitney)
- Nonparametric anova
- 1-way (Kruskal–Wallis)
- 2-way (Friedman)
- Ordered alternative (Jonckheere–Terpstra)
|
|---|
|
|---|
| Bayesian inference | |
|---|
|
|
|
|---|
| Correlation | |
|---|
| Regression analysis |
- Errors and residuals
- Regression validation
- Mixed effects models
- Simultaneous equations models
- Multivariate adaptive regression splines (MARS)
|
|---|
| Linear regression | |
|---|
| Non-standard predictors |
- Nonlinear regression
- Nonparametric
- Semiparametric
- Isotonic
- Robust
- Heteroscedasticity
- Homoscedasticity
|
|---|
| Generalized linear model | |
|---|
| Partition of variance |
- Analysis of variance (ANOVA, anova)
- Analysis of covariance
- Multivariate ANOVA
- Degrees of freedom
|
|---|
|
|
Categorical / Multivariate / Time-series / Survival analysis |
|---|
| Categorical |
- Cohen's kappa
- Contingency table
- Graphical model
- Log-linear model
- McNemar's test
- Cochran-Mantel-Haenszel statistics
|
|---|
| Multivariate |
- Regression
- Manova
- Principal components
- Canonical correlation
- Discriminant analysis
- Cluster analysis
- Classification
- Structural equation model
- Multivariate distributions
|
|---|
| 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)
- Cross-correlation (XCF)
- ARMA model
- ARIMA model (Box–Jenkins)
- Autoregressive conditional heteroskedasticity (ARCH)
- Vector autoregression (VAR)
|
|---|
| Frequency domain | |
|---|
|
|---|
| Survival | | Survival function |
- Kaplan–Meier estimator (product limit)
- Proportional hazards models
- Accelerated failure time (AFT) model
- First hitting time
|
|---|
| Hazard function | |
|---|
| Test | |
|---|
|
|---|
|
|
Applications |
|---|
| Biostatistics | |
|---|
| Engineering statistics |
- Chemometrics
- Methods engineering
- Probabilistic design
- Process / quality control
- Reliability
- System identification
|
|---|
| Social statistics | |
|---|
| Spatial statistics |
- Cartography
- Environmental statistics
- Geographic information system
- Geostatistics
- Kriging
|
|---|
|
|