VARIANCE COMPONENTS AND MIXED MODEL ANOVA/ANCOVA.

SURVIVAL/FAILURE TIME ANALYSIS.

GENERAL NONLINEAR ESTIMATION (and Quick Logit/Probit Regression).

Estimation Methods. The models can be fit using least squares or maximum-likelihood estimation, or any user-specified loss function. When using the least-squares criterion, the very efficient Levenberg-Marquardt and Gauss-Newton algorithms can be used to estimate the parameters for arbitrary linear and nonlinear regression problems. For large datasets or for difficult nonlinear regression problems (such as those rated "higher difficulty" among the Statistical Reference Datasets provided by the National Institute of Standards and Technology; see http://www.nist.gov/itl/div898/strd/index.html), when using the least-squares criterion, this is the recommended method for computing precise parameter estimates. When using arbitrary loss functions, the user can choose from among four very different, powerful estimation procedures (quasi-Newton, Simplex, Hooke-Jeeves pattern moves, and Rosenbrock pattern search method of rotating coordinates) so that stable parameter estimates can be obtained in practically all cases, and even in extremely numerically-demanding conditions (see the Validation Benchmarks ).

Models. The user can specify any type of model by typing in the respective equation into an equation editor. The equations may include logical operators; thus, discontinuous (piecewise) regression models and models including indicator variables can also be estimated. The equations may also include a wide selection of distribution functions and cumulative distribution functions (Beta, Binomial, Cauchy, Chi-square, Exponential, Extreme value, F, Gamma, Geometric, Laplace, Logistic, Normal, Log-Normal, Pareto, Poisson, Rayleigh, t (Student), or Weibull distribution). The user has full control over all aspects of the estimation procedure (e.g., starting values, step sizes, convergence criteria, etc.). The most common nonlinear regression models are predefined in the Nonlinear Estimation module, and can be chosen simply as menu options. Those regression models include stepwise Probit and Logit regression, the exponential regression model, and linear piecewise (break point) regression. Note that STATISTICA also includes implementations of powerful algorithms for fitting generalized linear models, including probit and multinomial logit models, and generalized additive models; see the respective descriptions for additional details.

Results. In addition to various descriptive statistics, standard results of the nonlinear estimation include the parameter estimates and their standard errors (computed independently of the estimation itself, via finite differencing to optimize precision; see the Validation Benchmarks ); the variance/covariance matrix of parameter estimates, the predicted values, residuals, and appropriate measures of goodness-of-fit (e.g., log-likelihood of estimated/null models and Chi-square test of difference, proportion of variance accounted for, classification of cases and odds-ratios for Logit and Probit models, etc.). Predicted and residual values can be appended to the data file for further analyses. For Probit and Logit models, the incremental fit is also automatically computed when adding or deleting parameters from the regression model (thus, the user can explore the data via a stepwise nonlinear estimation procedure; options for automatic forward and backward stepwise regression as well as best-subset selection of predictors in logit and probit models is provided in the Generalized Linear Models module, below).

Graphs. All output is integrated with extensive selections of graphs, including interactively-adjustable 2D and 3D (surface) arbitrary function fitting graphs which allow the user to visualize the quality of the fit and identify outliers or ranges of discrepancy between the model and the data; the user can interactively adjust the equation of the fitted function (as shown in the graph) without re-processing the data and visualize practically all aspects of the nonlinear fitting process). Many other specialized graphs are provided to evaluate the fitting process and visualize the results, such as histograms of all selected variables and residual values, scatterplots of observed versus predicted values and predicted versus residual values, normal and half-normal probability plots of residuals, and many others.

LOG-LINEAR ANALYSIS OF FREQUENCY TABLES.

TIME SERIES ANALYSIS/FORECASTING.

Transformations, Modeling, Plots, Autocorrelations. The available time series transformations allow the user to fully explore patterns in the input series, and to perform all common time series transformations, including: de-trending, removal of autocorrelation, moving average smoothing (unweighted and weighted, with user-defined or Daniell, Tukey, Hamming, Parzen, or Bartlett weights), moving median smoothing, simple exponential smoothing (see also the description of all exponential smoothing options below), differencing, integrating, residualizing, shifting, 4253H smoothing, tapering, Fourier (and inverse) transformations, and others. Autocorrelation, partial autocorrelation, and crosscorrelation analyses can also be performed.

ARIMA and Interrupted Time Series (Intervention) Analysis. The Time Series module offers a complete implementation of ARIMA. Models may include a constant, and the series can be transformed prior to the analysis; these transformations will automatically be "undone" when ARIMA forecasts are computed, so that the forecasts and their standard errors are expressed in terms of the values of the original input series. Approximate and exact maximum-likelihood conditional sums of squares can be computed, and the ARIMA implementation in the Time Series module is uniquely suited to fitting models with long seasonal periods (e.g., periods of 30 days). Standard results include the parameter estimates and their standard errors and the parameter correlations. Forecasts and their standard errors can be computed and plotted, and appended to the input series. In addition, numerous options for examining the ARIMA residuals (for model adequacy) are available, including a large selection of graphs. The implementation of ARIMA in the Time Series module also allows the user to perform interrupted time series (intervention) analysis. Several simultaneous interventions may be modeled, which can either be single-parameter abrupt-permanent interventions, or two-parameter gradual or temporary interventions (graphs of different impact patterns can be reviewed). Forecasts can be computed for all intervention models, which can be plotted (together with the input series) as well as appended to the original series.

Seasonal and Non-Seasonal Exponential Smoothing. The Time Series module contains a complete implementation of all 12 common exponential smoothing models. Models can be specified to contain an additive or multiplicative seasonal component and/or linear, exponential, or damped trend; thus, available models include the popular Holt-Winter linear trend models. The user may specify the initial value for the smoothing transformation, initial trend value, and seasonal factors (if appropriate). Separate smoothing parameters can be specified for the trend and seasonal components. The user can also perform a grid search of the parameter space in order to identify the best parameters; the respective results spreadsheet will report for all combinations of parameter values the mean error, mean absolute error, sum of squares error, mean square error, mean percentage error, and mean absolute percentage error. The smallest value for these fit indices will be highlighted in the spreadsheet. In addition, the user can also request an automatic search for the best parameters with regard to the mean square error, mean absolute error, or mean absolute percentage error (a general function minimization procedure is used for this purpose). The results of the respective exponential smoothing transformation, the residuals, as well as the requested number of forecasts, are available for further analyses and plots. A summary plot is also available to assess the adequacy of the respective exponential smoothing model; that plot will show the original series together with the smoothed values and forecasts, as well as the smoothing residuals plotted separately against the right-Y axis.

Classical Seasonal Decomposition (Census Method I). The user may specify the length of the seasonal period, and choose either the additive or multiplicative seasonal model. The program will compute the moving averages, ratios or differences, seasonal factors, the seasonally adjusted series, the smoothed trend-cycle component, and the irregular component. Those components are available for further analysis; for example, the user may compute histograms, normal probability plots, etc. for any or all of these components (e.g., to test model adequacy).

X-11 Monthly and Quarterly Seasonal Decomposition and Seasonal Adjustment (Census Method II). The Time Series module contains a full-featured implementation of the US Bureau of the Census X-11 variant of the Census Method II seasonal adjustment procedure. While the original X-11 algorithms were not year-2000 compatible (only data prior to January 2000 could be analyzed), the STATISTICA implementation of X11 can handle data containing dates prior to January 1, 2000, after that date, or series that will start prior to that date but terminate in or after the year 2000. The arrangement of options and dialogs closely follows the definitions and conventions described in the Bureau of the Census documentation. Additive and multiplicative seasonal models may be specified. The user may also specify prior trading-day factors and seasonal adjustment factors. Trading-day variation can be estimated via regression (controlling for extreme observations), and used to adjust the series (conditionally if requested). The standard options are provided for graduating extreme observations, for computing the seasonal factors, and for computing the trend-cycle component (the user can choose between various types of weighted moving averages; optimal lengths and types of moving averages can also automatically be chosen by the program). The final components (seasonal, trend-cycle, irregular) and the seasonally adjusted series are automatically available for further analyses and plots; those components can also be saved for further analyses with other programs. The program will produce the plots of the different components, including categorized plots by months (or quarters).

Polynomial Distributed Lag Models. The implementation of the polynomial distributed lag methods in the Time Series module will estimate models with unconstrained lags as well as (constrained) Almon distributed lags models. A selection of graphs are available to examine the distributions of the model variables.

Spectrum (Fourier) and Cross-Spectrum Analysis. The Time Series module includes a full implementation of spectrum (Fourier decomposition) analysis and cross-spectrum analysis techniques. The program is particularly suited for the analysis of unusually long time series (e.g., with over 250,000 observations), and it will not impose any constraints on the length of the series (i.e., the length of input series does not have to be a multiple of 2). However, the user may also choose to pad or truncate the series prior to the analysis. Standard pre-analysis transformations include tapering, subtraction of the mean, and detrending. For single spectrum analysis, the standard results include the frequency, period, sine and cosine coefficients, periodogram values, and spectral density estimates. The density estimates can be computed using Daniell, Hamming, Bartlett, Tukey, Parzen, or user-defined weights and user-defined window widths. An option that is particularly useful for long input series is to display only a user-defined number of the largest periodogram or density values in descending order; thus, the most salient periodogram or density peaks can be easily identified in long series. The user can compute the Kolmogorov-Smirnov d test for the periodogram values to test whether they follow an exponential distribution (i.e., whether the input is a white-noise series). Numerous plots are available to summarize the results; the user can plot the sine and cosine coefficients, periodogram values, log-periodogram values, spectral density values, and log-density values against the frequencies, period, or log-period. For long input series, the user can choose the segment (period) for which to plot the respective periodogram or density values, thus enhancing the "resolution" of the periodogram or density plot. For cross-spectrum analysis, in addition to the single spectrum results for each series, the program computes the cross-periodogram (real and imaginary part), co-spectral density, quadrature spectrum, cross-amplitude, coherency values, gain values, and the phase spectrum. All of these can also be plotted against the frequency, period, or log-period, either for all periods (frequencies) or only for a user-defined segment. A user-defined number of the largest cross-periodogram values (real or imaginary) can also be displayed in a spreadsheet in descending order of magnitude to facilitate the identification of salient peaks when analyzing long input series. As with all other procedures in the Time Series module, all of these result series can be appended to the active work area, and will be available for further analyses with other time series methods or other STATISTICA modules.