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发布时间:2021-06-03
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Posterior / Likelihood Prior
If the posterior distribution can be derived in a closed form, we may proceed directly to the
inference stage of Bayesian analysis. Unfortunately, except for some special models, the posterior
distribution is rarely available explicitly and needs to be estimated via simulations. MCMC sampling
can be used to simulate potentially very complex posterior models with an arbitrary level of precision.
MCMC methods for simulating Bayesian models are often demanding in terms of specifying an efficient
sampling algorithm and verifying the convergence of the algorithm to the desired posterior distribution.
Inference is the next step of Bayesian analysis. If MCMC sampling is used for approximating the
posterior distribution, the convergence of MCMC must be established before proceeding to inference.
Point and interval estimators are either derived from the theoretical posterior distribution or estimated
from a sample simulated from the posterior distribution. Many Bayesian estimators, such as posterior
In Stata 16, we introduce a new, unified suite of commands for modeling choice data. We have added new commands for summarizing choice data. We renamed and improved existing commands for fitting choice models. We even added a new command for fitting mixed logit models for panel data. And we document them together in the new Choice Models Reference Manual.
And here’s the best part: margins now works after fitting choice models. This means you can now easily interpret the results of your choice models. While the coefficients estimated in choice models are often almost uninterpretable, margins allows you to ask and answer very specific questions based on your results. Say that you are modeling choice of transportation. You can answer questions such as
• What proportion of travelers are expected to choose air travel?
• How does the probability of traveling by car change for each additional $10,000 in income?
• If wait times at the airport increase by 30 minutes, how does this affect the choice of each mode of transportation?
What else is new? You now cmset your data before fitting a choice model. For instance,
. cmset personid transportmethod
Then, you use cmsummarize, cmchoiceset, cmtab, and cmsample to explore, summarize, and look for potential problems in your data.
And you use cm estimation commands to fit one of the following choice models:
• cmclogit conditional logit (McFadden’s choice) model
• cmmixlogit mixed logit model
• cmxtmixlogit panel-data mixed logit model
• cmmprobit multinomial probit model
• cmroprobit rank-ordered probit model
• cmrologit rank-ordered logit model
Unlike the others, cmxtmixlogit is not renamed and improved. It is completely new in Stata 16, and
Frequentist analysis is entirely data-driven and strongly depends on whether or not the data
assumptions required by the model are met. On the other hand, Bayesian analysis provides a more
robust estimation approach by using not only the data at hand but also some existing information or
knowledge about model parameters.
In frequentist statistics, estimators are used to approximate the true values of the unknown parameters,
whereas Bayesian statistics provides an entire distribution of the parameters. In our example of a
prevalence of an infectious disease from What is Bayesian analysis?, frequentist analysis produced one
point estimate for the prevalence, whereas Bayesian analysis estimated the entire posterior distribution
of the prevalence based on a given sample.
值得信任
技术支持
我们不仅编写统计方法,我们还会进行验证。
您能从Stata estimator rest与其他估计的比较中看到Monte-Carlo模拟的一致性和覆盖率以及我们统计学家们进行的广
泛测试。每一版的Stata软件,我们都通过了各种认证,包括230万行的代码测试,并产生了430万行的结果输出。我们验
证了这430万行代码中的每一个数字和每一段文字。
Stata公司的技术团队对注册用户提供免费服务。而这远比您付的费用得到的更多。
我们有的Stata程序员和统计学家来回答您的技术问题。从棘手的数据管理方案到让您做出的图表;从解释大
量的标准错误到您进行多层模型,我们认真回答您的问题。
系统要求
■ Stata for Windows
■ Stata for Mac
■ Stata for Linux
Windows 10* / Windows 8* / Windows 7* / Windows Vista* / Windows
Server 2016, 2012, 2008, 2003 * ;* 64-bit and 32-bit Windows varieties for
x86-64 and x86 processors made by Intel® and AMD Find out if your OS is
64-bit compliant.
Stata for macOS requires 64-bit Intel® processors (Core™2 Duo or better)
running macOS 10.9 or newer Find out if your OS is 64-bit compliant.
Any 64-bit (x86-64 or compatible) or 32-bit (x86 or compatible) running
Linux For xstata, you need to have GTK 2.24 installed
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