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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

We consider two types of CRIs. The first one is based on quantiles. The second one is the highest
posterior density (HPD) interval.
An f(1 �� ) 100g% quantile-based, or also known as an equal-tailed CRI, is defined as
(q=2; q1��=2), where qa denotes the ath quantile of the posterior distribution. A commonly reported
equal-tailed CRI is (q0:025; q0:975).
HPD interval is defined as an f(1 �� ) 100g% CRI of the shortest width. As its name implies,
this interval corresponds to the region of the posterior density with the highest concentration. For a
unimodal posterior distribution, HPD is unique, but for a multimodal distribution it may not be unique.
Computational approaches for calculating HPD are described in Chen and Shao (1999) and Eberly
and Casella (2003).

Remarks and examples
Remarks are presented under the following headings:
What is Bayesian analysis?
Bayesian versus frequentist analysis, or why Bayesian analysis?
How to do Bayesian analysis
Advantages and disadvantages of Bayesian analysis
Brief background and literature review
Bayesian statistics
Posterior distribution
Selecting priors
Point and interval estimation
Comparing Bayesian models
Posterior prediction
Bayesian computation
Markov chain Monte Carlo methods
Metropolis–Hastings algorithm
Adaptive random-walk Metropolis–Hastings
Blocking of parameters
Metropolis–Hastings with Gibbs updates
Convergence diagnostics of MCMC
Summary
The first five sections provide a general introduction to Bayesian analysis. The remaining sections
provide a more technical discussion of the concepts of Bayesian analysis.

New reporting features in Stata 16:
• The dyndoc and markdown commands now create Word documents in addition to the HTML documents they previously created. Now, you can easily incorporate full Stata output and graphs with Markdown-formatted text to create customized Word documents.
• The Do-file Editor now provides syntax highlighting for Markdown language elements.
• The putdocx command now lets you include headers, footers, and page numbers. It also makes it easier to write large blocks of text.
• The html2docx command converts HTML documents, including CSS, to Word documents.
• The docx2pdf command converts Word documents to PDFs.
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