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关 键 词：stata秩和检验
行 业：IT 软件 身份证书管理系统
发布时间：2022-01-12
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主讲嘉宾
王存同，人口学博士、教授。博士毕业于北京大学（与University of Michigan合作培养)，博士后研究员就职于美国伊利诺伊大学（University of Illinois at Urbana-Champaign）。现为财经大学社会发展学院教授，主要从事社会统计及计量经济分析、人口社会学、人口经济学等领域的研究与教学，其定量研究与教学在国内外学术界享有较高的盛誉。
What is Bayesian analysis?
Bayesian analysis is a statistical analysis that answers research questions about unknown parameters
of statistical models by using probability statements. Bayesian analysis rests on the assumption that
all model parameters are random quantities and thus are subjects to prior knowledge. This assumption
is in sharp contrast with the more traditional, also called frequentist, statistical inference where all
parameters are considered unknown but fixed quantities. Bayesian analysis follows a simple rule
of probability, the Bayes rule, which provides a formalism for combining prior information with
evidence from the data at hand. The Bayes rule is used to form the so called posterior distribution of
model parameters. The posterior distribution results from updating the prior knowledge about model
parameters with evidence from the observed data. Bayesian analysis uses the posterior distribution to
form various summaries for the model parameters including point estimates such as posterior means,
medians, percentiles, and interval estimates such as credible intervals. Moreover, all statistical tests
about model parameters can be expressed as probability statements based on the estimated posterior
distribution.
Frequentist hypothesis testing is based on a deterministic decision using a prespecified significance
level of whether to accept or reject the null hypothesis based on the observed data, assuming that
the null hypothesis is actually true. The decision is based on a p-value computed from the observed
data. The interpretation of the p-value is that if we repeat the same experiment and use the same
testing procedure many times, then given our null hypothesis is true, we will observe the result (test
statistic) as extreme or more extreme than the one observed in the sample (100 p-value)% of the
times. The p-value cannot be interpreted as a probability of the null hypothesis, which is a common
misinterpretation. In fact, it answers the question of how likely are our data given that the null
hypothesis is true, and not how likely is the null hypothesis given our data. The latter question can
be answered by Bayesian hypothesis testing, where we can compute the probability of any hypothesis
of interest.
Most applications of rank() will be to one variable, but the argument exp can be more general,
namely, an expression. In particular, rank(-varname) reverses ranks from those obtained by
rank(varname).
The default ranking and those obtained by using one of the track, field, and unique options
differ principally in their treatment of ties. The default is to assign the same rank to tied values
such that the sum of the ranks is preserved. The track option assigns the same rank but resembles
the convention in track events; thus, if one person had the lowest time and three persons tied for
second-lowest time, their ranks would be 1, 2, 2, and 2, and the next person(s) would have rank 5.
The field option acts similarly except that the highest is assigned rank 1, as in field events in which
the greatest distance or height wins. The unique option breaks ties arbitrarily: its most obvious use
is assigning ranks for a graph of ordered values. See also group() for another kind of “ranking”.
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