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New in Stata 11 (新功能介绍)
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Stata 11 新功能介绍
Stata 11 description
Stata 11 is designed for medical researchers, biostatisticians, epidemiologists, economists, sociologists, political scientists, geographers, psychologists, social scientists, and other research professionals.
It is available for Windows, Macintosh, and Unix computers and provides full data management, graphics, statistical, and matrix language capabilities. In addition to general-purpose capabilities such as summary statistics, ANOVA, linear, logistic, and probit regression, and the like, Stata provides survival analysis including Kaplan-Meier survivor function estimates, Cox proportional hazards models, survey analysis, time series, multivariate analysis, and panel data estimators including random-effects, fixed-effects, and multilevel mixed-effects for continuous, binary, and count outcomes.
3.广义矩法（Generalized method of moments,GMM）
6.竞争风险回归（Competing-risks regression）
10.动态因素模型（Dynamic-factor models）
12.多变量GARCH模型（Multivariate GARCH models）
14.面板数据根单位测试（Panel-data unit-root tests）
15.Mata
16.Stata/MP
Major Features:
1. Data management: data transformations, match-merge, ODBC, XML, by-group processing, append files, sort, row–column transposition, labeling, saving results, more
2. Basic statistics: summaries, cross-tabulations, correlations, t tests, equality-of-variance tests, tests of proportions, confidence intervals, factor variables, more
3. Linear models: regression; bootstrap, jackknife, and robust Huber/White/sandwich variance estimates; instrumental variables; three-stage least squares; constraints; quantile regression; GLS; more
4. Multilevel mixed-effects models: continuous, binary, and count outcomes; two-, three-, and multiway random-intercepts and random-coefficients models; crossed random effects; ML and REML estimation; BLUPs of effects and fitted values; hierarchical models; residual error structures; more
5. Binary, count, and limited dependent variables: logistic, probit, tobit; Poisson and negative binomial; conditional, multinomial, nested, ordered, rank-ordered, and stereotype logistic; multinomial probit; zero-inflated and zero-truncated count models; selection models; marginal effects; more
6. Panel data/longitudinal data: random- and fixed-effects with robust standard errors, linear mixed models, random-effects probit, GEE, random- and fixed-effects Poisson, dynamic panel-data models, and instrumental-variables regression; panel unit-root tests; AR(1) disturbances; more
7. Generalized linear models (GLMs): ten link functions, user-defined links, seven distributions, ML and IRLS estimation, nine variance estimators, seven residuals, more
8. Nonparametric methods: Wilcoxon–Mann–Whitney, Wilcoxon signed ranks and Kruskal–Wallis tests; Spearman and Kendall correlations; Kolmogorov–Smirnov tests; exact binomial CIs; more
9. Exact statistics: exact logistic and Poisson regression, exact case–control statistics, binomial tests, Fisher’s exact test for r × c tables, more
10. ANOVA/MANOVA: balanced and unbalanced designs; factorial, nested, and mixed designs; repeated measures; marginal means; more
11. Multivariate methods: factor analysis, principal components, discriminant analysis, rotation, multidimensional scaling, Procrustean analysis, correspondence analysis, biplots, dendrograms, user-extensible analyses, more
12. Cluster analysis: hierarchical clustering; kmeans and kmedian nonhierarchical clustering; dendrograms; stopping rules; user-extensible analyses; more
13. Resampling and simulation methods: bootstrapping, jackknife and Monte Carlo simulation, permutation tests, more
14. Model testing and postestimation support: Wald tests; LR tests; linear and nonlinear combinations, tests, and predictions; marginal means, least-squares means, adjusted means, average partial and marginal effects; Hausman tests; more
15. Graphics: line charts, scatterplots, bar charts, pie charts, hi–lo charts, Graph Editor, regression diagnostic graphs, survival plots, nonparametric smoothers, distribution Q–Q plots, more
16. Survey methods: sampling weights, multistage designs; stratification, poststratification; deff; means, proportions, ratios, totals; summary tables; predictive margins; bootstrap, jackknife, and linearization-based variance estimation; regression, instrumental variables, probit, Cox regression; more
17. Survival analysis: Kaplan–Meier and Nelson–Aalen estimators, Cox regression (frailty); parametric models (frailty); competing risks; hazards; time-varying covariates; left and right censoring, Weibull, exponential, and Gompertz analysis; sample size and power analysis; more
18. Tools for epidemiologists: standardization of rates, case–control, cohort, matched case–control, Mantel–Haenszel, pharmacokinetics, ROC analysis, ICD-9-CM, more
19. Time series: ARIMA, ARCH/GARCH, VAR, VECM, multivariate GARCH, dynamic factors, state-space models, high-frequency data, correlograms, periodograms, white-noise tests, unit-root tests, Holt–Winters smoothers, Haver Analytics data, rolling and recursive estimation, more
20. Multiple imputation: five univariate imputation methods, multivariate normal imputation, explore pattern of missingness, manage imputed datasets, estimate model and pool results, transform parameters, joint tests of parameter estimates, more
21. Maximum likelihood: user-specified functions; NR, DFP, BFGS, BHHH; OIM, OPG, robust, bootstrap, and jackknife matrices; Wald tests; survey data; numeric or analytic derivatives; more
22. Other statistical methods: generalized method of moments (GMM), sample size and power, nonlinear regression, stepwise regression, statistical and mathematical functions, more
23. Programming language: adding new commands, command scripting, if, while, command parsing, debugging, menu and dialog-box programming, markup and control language, more
24. Matrix programming—Mata: interactive sessions, large-scale development projects, optimization, matrix inversions, decompositions, eigenvalues and eigenvectors, LAPACK engine, real and complex numbers, string matrices, interface to Stata datasets and matrices, numerical derivatives, object-oriented programming, more
25. Internet capabilities: ability to install new commands, web updating, web file sharing, latest Stata news, more
26. Accessibility: Section 508 compliance, accessibility for persons with disabilities
27. Sample session: A sample session of Stata for Mac, Unix, or Windows.
28. User-written commands: User-written commands for meta-analysis, data management, survival, econometrics, more
Enhancements:
* Fonts in graphics
*
o Italics and bold
o Greek letters
o Mathematical symbols
o Superscripts and subscripts
o Multiple font faces
o More...
* Competing-risks regression
*
o Time-varying covariates
o Cumulative incidence graphs
o Predict relative subhazards
o More...
* PDF documentation
*
o Comes with every copy of Stata
o Includes all manuals
o Linked from help files
o More...
* State-space models
*
o VARMA Models
o Structural time-series models
o Unobserved component models
o Stationary and nonstationary cases
o Kalman filter
o More...
* Variables Manager
*
o Change storage types, names, and formats
o Add and edit value labels
o Attach notes to variables
o Filter variables
o More...
* Dynamic-factor models
*
o Unobserved dynamic factors
o Static factor models
o Extracting unobserved factors
o More...
* Data Editor
*
o Work reproducibly
o Live view of data while you perform analyses
o Filters to focus on subsets of data
o Undo with snapshots
o Enter dates and times easily
o More...
o
* Multivariate GARCH models
*
o Diagonal vech model
o ARCH and GARCH terms
o Predict conditional volatitlity
o More...
o Do-file Editor
o Syntax highlighting
o Unlimited file size
o Line bookmarks
o Code folding
o More...
* Panel-data unit-root tests
*
o Levin–Lin–Chu, Harris–Tzavalis, Breitung, and Im–Pesaran–Shin tests for unit roots
o Fisher-type tests based on augmented Dickey–Fuller and Phillips–Perron tests
o Hadri LM test for stationarity
o More...
* Mata
*
o Object-oriented programming
o Numerical derivative functions
o Associative arrays
o LAPACK functions
o More...
* Stata/MP
*
o Multiple imputation
o State-space models
o Dynamic-factor models
Source: http://www.cheerchain.com.tw/forums/viewthread.php?tid=650&extra=
http://www.ritme.com/download/docpdf/statanews.announce11.pdf
https://wap.sciencenet.cn/blog-285749-251173.html
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