Not to be confused with linear model of innovation.
In statistics, the term linear model is used in different ways according to the context. The most common occurrence is in connection with regression models and the term is often taken as synonymous with linear regression model. However, the term is also used in time series analysis with a different meaning. In each case, the designation "linear" is used to identify a subclass of models for which substantial reduction in the complexity of the related statistical theory is possible.
Contents
- 1 Linear regression models
- 2 Time series models
- 3 Other uses in statistics
- 4 See also
- 5 References
|
Linear regression models [edit]
For the regression case, the statistical model is as follows. Given a (random) sample the relation between the observations Yi and the independent variables Xij is formulated as
where may be nonlinear functions. In the above, the quantities εi are random variables representing errors in the relationship. The "linear" part of the designation relates to the appearance of the regression coefficients, βj in a linear way in the above relationship. Alternatively, one may say that the predicted values corresponding to the above model, namely
are linear functions of the βj.
Given that estimation is undertaken on the basis of a least squares analysis, estimates of the unknown parameters βj are determined by minimising a sum of squares function
From this, it can readily be seen that the "linear" aspect of the model means the following:
-
- the function to be minimised is a quadratic function of the βj for which minimisation is a relatively simple problem;
- the derivatives of the function are linear functions of the βj making it easy to find the minimising values;
- the minimising values βj are linear functions of the observations Yi;
- the minimising values βj are linear functions of the random errors εi which makes it relatively easy to determine the statistical properties of the estimated values of βj.
Time series models [edit]
An example of a linear time series model is an autoregressive moving average model. Here the model for values {Xt} in a time series can be written in the form
where again the quantities εt are random variables representing innovations which are new random effects that appear at a certain time but also affect values of X at later times. In this instance the use of the term "linear model" refers to the structure of the above relationship in representing Xt as a linear function of past values of the same time series and of current and past values of the innovations.[1] This particular aspect of the structure means that it is relative simple to derive relations for the mean and covariance properties of the time series. Note that here the "linear" part of the term "linear model" is not referring to the coefficients φi and θi, as it would be in the case of a regression model, which looks structurally similar.
Other uses in statistics [edit]
There are some other instances where "nonlinear model" is used to contrast with a linearly structured model, although the term "linear model" is not usually applied. One example of this is nonlinear dimensionality reduction.
See also [edit]
- General linear model
- Linear system
- Statistical model
References [edit]
- ^ Priestley, M.B. (1988) Non-linear and Non-stationary time series analysis, Academic Press. ISBN 0-12-564911-8
Statistics
|
|
Descriptive statistics
|
|
Continuous data |
Location |
- Mean (Arithmetic, Geometric, Harmonic)
- Median
- Mode
|
|
Dispersion |
- Range
- Standard deviation
- Coefficient of variation
- Percentile
- Interquartile range
|
|
Shape |
- Variance
- Skewness
- Kurtosis
- Moments
- L-moments
|
|
|
Count data |
|
|
Summary tables |
- Grouped data
- Frequency distribution
- Contingency table
|
|
Dependence |
- Pearson product-moment correlation
- Rank correlation (Spearman's rho, Kendall's tau)
- Partial correlation
- Scatter plot
|
|
Statistical graphics |
- Bar chart
- Biplot
- Box plot
- Control chart
- Correlogram
- Forest plot
- Histogram
- Q–Q plot
- Run chart
- Scatter plot
- Stemplot
- Radar chart
|
|
|
|
Data collection
|
|
Designing studies |
- Effect size
- Standard error
- Statistical power
- Sample size determination
|
|
Survey methodology |
- Sampling
- Stratified sampling
- Opinion poll
- Questionnaire
|
|
Controlled experiment |
- Design of experiments
- Randomized experiment
- Random assignment
- Replication
- Blocking
- Factorial experiment
- Optimal design
|
|
Uncontrolled studies |
- Natural experiment
- Quasi-experiment
- Observational study
|
|
|
|
Statistical inference
|
|
Statistical theory |
- Sampling distribution
- Order statistics
- Sufficiency
- Completeness
- Exponential family
- Permutation test (Randomization test)
- Empirical distribution
- Bootstrap
- U statistic
- Efficiency
- Asymptotics
- Robustness
|
|
Frequentist inference |
- Unbiased estimator (Mean unbiased minimum variance, Median unbiased)
- Biased estimators (Maximum likelihood, Method of moments, Minimum distance, Density estimation)
- Confidence interval
- Testing hypotheses
- Power
- Parametric tests (Likelihood-ratio, Wald, Score)
|
|
Specific tests |
- Z (normal)
- Student's t-test
- F
- Chi-squared
- Signed-rank (1-sample, 2-sample, 1-way anova)
- Shapiro–Wilk
- Kolmogorov–Smirnov
|
|
Bayesian inference |
- Bayesian probability
- Prior
- Posterior
- Credible interval
- Bayes factor
- Bayesian estimator
- Maximum posterior estimator
|
|
|
|
Correlation and regression analysis
|
|
Correlation |
- Pearson product–moment correlation
- Partial correlation
- Confounding variable
- Coefficient of determination
|
|
Regression analysis |
- Errors and residuals
- Regression model validation
- Mixed effects models
- Simultaneous equations models
|
|
Linear regression |
- Simple linear regression
- Ordinary least squares
- General linear model
- Bayesian regression
|
|
Non-standard predictors |
- Nonlinear regression
- Nonparametric
- Semiparametric
- Isotonic
- Robust
|
|
Generalized linear model |
- Exponential families
- Logistic (Bernoulli)
- Binomial
- Poisson
|
|
Partition of variance |
- Analysis of variance (ANOVA)
- Analysis of covariance
- Multivariate ANOVA
- Degrees of freedom
|
|
|
|
Categorical, multivariate, time-series, or survival analysis
|
|
Categorical data |
- Cohen's kappa
- Contingency table
- Graphical model
- Log-linear model
- McNemar's test
|
|
Multivariate statistics |
- Multivariate regression
- Principal components
- Factor analysis
- Cluster analysis
- Classification
- Copulas
|
|
Time series analysis |
General |
- Decomposition
- Trend
- Stationarity
- Seasonal adjustment
|
|
Time domain |
- ACF
- PACF
- XCF
- ARMA model
- ARIMA model
- Vector autoregression
|
|
Frequency domain |
- Spectral density estimation
|
|
|
Survival analysis |
- Survival function
- Kaplan–Meier
- Logrank test
- Failure rate
- Proportional hazards models
- Accelerated failure time model
|
|
|
|
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
- National accounts
- Official statistics
- Population
- Psychometrics
|
|
Spatial statistics |
- Cartography
- Environmental statistics
- Geographic information system
- Geostatistics
- Kriging
|
|
|
|
- Category
- Portal
- Outline
- Index
|
|