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Stata 12 正式发布! |
2011-07-28
广州博脉 |
 w_ww._p_o_m_i_n_e._co_m__ 知名统计软件 Stata 12 已经发布!Stata 12 的新特性如下: w_w_w.po_m_i__n__e._c__o__m_ New in Stata 12 w_w__w_.p_om_i__ne_._c__o_m Stata 12 now shipping. Highlights are shown below. Select any header to find out more. w_ww._pom_i__ne__._c_om_ PDF of the Stata 12 announcement—click here. w_w_w._po_m__i_n_e._c_o_m Also visit Overview: Why use Stata? and details of Stata’s capabilities. Structural equation modeling (SEM) w__w_w_._po__m__ine_._com_ sem (L1 -> m1 m2) (L2 -> m3 m4) (L3 <- L1 L2) (L3 -> m5 m6 m7) ww_w._p_o_mi_n_e_._c__o_m 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), ... More ... w__w_w_._p__om_i_ne__._co__m Contrasts ww_w.pom_i_n_e__._com Compare reference or adjacent categories Compare to grand mean Orthogonal polynomials Treatment effects More ... w_w_w_.p_o_m__i_n__e_.__c__o_m_ Pairwise comparisons w_ww_.p_o__mi_n_e_._c__o_m__ Compare means, intercepts, or slopes Compare odds ratios Bonferroni, Scheffé, Tukey, Dunnett, and other adjustments More ... w_w_w_._p_o__mi_n__e_._c_o_m Margins plots w_ww._po_min__e_.co_m_ Profile and interaction plots Margins, contrasts, and pairwise comparisons Potential outcomes Comparative graphs More ... w_ww_._p_om_i_n__e.c__o_m_ Multiple imputation www__.p_o__mi_n__e._c_o_m_ 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 ... ww_w.__po_mi__n_e.c_o__m ROC analysis w_w_w__._po_m_i_n_e_._c_om Parametric and nonparametric Adjustments for covariates Case-control regression models Bootstrap and model-based SEs Area under the curve (AUC) and partial AUC More ... ww__w_._p_omi_n__e__._com_ Contour plots Multilevel mixed-effects models w__ww._p_om_i_n_e__.__c_o__m_ Complex survey data Frequency and sampling weights Robust and clustered SEs Weighting at each level Residual covariance structures: exponential, banded, and Toeplitz More ... w_w_w_.__po__mi_ne_._c_om__ Excel® import/export ww_w__.p__o__mi_n_e_.com__ Preview tool Adjust import based on preview More data management: ODBC connections strings, EBCDIC, rename groups of variables, ... More ... w_ww_.pom_i_n_e.__co_m Unobserved components model (UCM) w__w_w._p__o__m__i__n_e_._c__om_ Trend, seasonal, and cyclical components Static and dynamic forecasts of components Stochastic cycles More ... w_w_w._po_min_e_.__c_o_m_ Automatic memory management w_w_w__.p__o_m_i__n_e_._co_m Automatically adjusts to dataset size Tunable Up to 1 terabyte of memory w__w__w_._p_om_in__e._c_om_ ARFIMA w__ww_._p__o__m_in__e__._c_o__m_ Long-memory processes Fractional integration Robust variance estimates Static and dynamic forecasts Linear constraints More ... w_w_w_._po__m_i_n_e._c__o_m__ Interface ww_w_.p_omi__n_e.__com_ 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 w_ww__.__p__o_m_in_e_.co__m_ Multivariate GARCH w_w_w_.__po_min__e_.__co_m__ 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 ... w_ww._pom__in__e._c_o_m Spectral density w__ww.po_m_i__n__e_.__c__o_m_ Parametric estimates after ARIMA, ARFIMA, and UCM Assess importance of frequencies More ... w_w__w.pomi__ne.c_om_ Installation Qualification w_w_w._p_o_m__ine__.c_o_m Downloadable tool Report for submission to regulatory agencies More ... w_ww_.__p__o_m_i__n__e.__c_o_m_ Time-series filters w_w_w_._pomi_ne.c_o_m_ Trend and cycle decompositions Christiano–Fitzgerald band-pass filter Baxter–King band-pass filter Hodrick–Prescott high-pass filter Butterworth high-pass filter More ... w_w_w_.po_mi__n_e__._c__o__m_ Stata/MP w__w__w_.__pom_i_ne_.co_m_ More estimators Up to 64 cores More ... w_w_w__._po_m__in_e__.c_o_m_ Business calendars www.p__o__mi_n_e._c_o_m_ Trading days User definable Lags and leads using business days Conversions from standard calendar More ... w_ww.__p_o_m_i_ne_.c__om More w_w__w.po__m_in__e._co__m_ 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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