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Stata 12 正式发布！ |
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2011-07-28
广州博脉 |
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知名统计软件 Stata 12 已经发布！Stata 12 的新特性如下：
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New in Stata 12
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Stata 12 now shipping. Highlights are shown below. Select any header to find out more.
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PDF of the Stata 12 announcement—click here.
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Also visit Overview: Why use Stata? and details of Stata’s capabilities.
Structural equation modeling (SEM)
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sem (L1 -> m1 m2)
(L2 -> m3 m4)
(L3 <- L1 L2)
(L3 -> m5 m6 m7)
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Path diagrams
Graphical model builder
Standardized and unstandardized estimates
Modification indices
Direct and indirect effects
Score tests and Wald tests
Factors scores and other predictions
Goodness of fit
Estimation with groups and tests of invariance
Survey data and clustered data
Raw or statistical summary data
FIML estimation with missing at random (MAR) data
Maximum likelihood, ADF, and GMM estimation
Flexible extension of multivariate regression, instrumental variables, and simultaneous systems
Confirmatory factor analysis (CFA), correlated uniqueness models, latent growth models, multiple indicators and multiple causes (MIMIC), ...
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Contrasts
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Compare reference or adjacent categories
Compare to grand mean
Orthogonal polynomials
Treatment effects
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Pairwise comparisons
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Compare means, intercepts, or slopes
Compare odds ratios
Bonferroni, Scheffé, Tukey, Dunnett, and other adjustments
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Margins plots
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Profile and interaction plots
Margins, contrasts, and pairwise comparisons
Potential outcomes
Comparative graphs
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Multiple imputation
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Chained equations
Conditional imputation
Impute separately within groups
Linear and nonlinear predictions
Measure simulation error
Panel data and multilevel models
Impute continuous, ordinal, cardinal, and count variables
More ...
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ROC analysis
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Parametric and nonparametric
Adjustments for covariates
Case-control regression models
Bootstrap and model-based SEs
Area under the curve (AUC) and partial AUC
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Contour plots
Multilevel mixed-effects models
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Complex survey data
Frequency and sampling weights
Robust and clustered SEs
Weighting at each level
Residual covariance structures: exponential, banded, and Toeplitz
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Excel® import/export
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Preview tool
Adjust import based on preview
More data management: ODBC connections strings, EBCDIC, rename groups of variables, ...
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Unobserved components model (UCM)
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Trend, seasonal, and cyclical components
Static and dynamic forecasts of components
Stochastic cycles
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Automatic memory management
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Automatically adjusts to dataset size
Tunable
Up to 1 terabyte of memory
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ARFIMA
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Long-memory processes
Fractional integration
Robust variance estimates
Static and dynamic forecasts
Linear constraints
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Interface
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Manage variables, storage types, notes, and formats without leaving the main interface
Select variables using filters
Filter prior commands and search results
Tabbed Viewer
Jump to dialogs, related commands, and sections in the online help
Hide, show, reorder, and filter variables in the Data Editor
Preview before pasting data
PDF export of results and graphs
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Multivariate GARCH
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Constant conditional correlations (CCC)
Dynamic conditional correlations (DCC)
Varying conditional correlations (VCC)
Multivariate normal and Students’ t errors
Robust variance estimates
Level and variance predictions
Static and dynamic forecasts
More ...
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Spectral density
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Parametric estimates after ARIMA, ARFIMA, and UCM
Assess importance of frequencies
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Installation Qualification
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Downloadable tool
Report for submission to regulatory agencies
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Time-series filters
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Trend and cycle decompositions
Christiano–Fitzgerald band-pass filter
Baxter–King band-pass filter
Hodrick–Prescott high-pass filter
Butterworth high-pass filter
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Stata/MP
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More estimators
Up to 64 cores
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Business calendars
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Trading days
User definable
Lags and leads using business days
Conversions from standard calendar
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More
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General statistics: functions for Tukey's Studentized range and Dunnett's multiple range, baseline odds for logistic regression, ...
Survey data: support for SEM, bootstrap and successive difference replicate (SDR) weights, goodness of fit after binary models, coefficient of variation,
Count data: truncated count-data regressions, probability predictions, robust and cluster-robust SEs for fixed-effects Poisson regression, ...
Panel data: probability predictions, multiple imputation support, ...
Survival data: goodness-of-fit statistic that is robust to censoring
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