| 1. | If we wish to avoid the inaccuracies caused by the size of the sample, we need to use the t distribution .
为了避免因样本容量大小而造成的误差，我们必须使用T分布。 |
| 2. | In general, the t distribution is flatter than the normal distribution, and there is a different t distribution for every possible sample size .
一般地说，t分布比正常分布更平坦一些，对于不同的样本容易都有一个不同的相应t分布。 |
| 3. | Some notes on parameter estimation in a class of linear models with an error vector having multivariate t distribution
分布的一类线性模型下参数估计的若干注记 |
| 4. | If we wish to avoid the inaccuracies caused by the size of the sample , we need to use the t distribution
为了避免因样本容量大小而造成的误差，我们必须使用t分布。 |
| 5. | In general , the t distribution is flatter than the normal distribution , and there is a different t distribution for every possible sample size
一般地说， t分布比正常分布更平坦一些，对于不同的样本容易都有一个不同的相应t分布。 |
| 6. | Last , student - t distribution and ged can better describe the distribution of return rate in china ' s stock market . in a word , all the conclusion above indicates that the method in this paper is valid and credible
最后，利用学生氏- t分布，广义误差分布来模拟中国证券市场的指数收益率的分布提高了var的计算精度。 |
| 7. | The first function returns a probability value associated with a t statistic based upon the students t distribution , while the second inverse function computes the t statistic corresponding to a given alpha setting
第一个函数根据学生的t分布返回了与t统计值相关的概率值，而第二个反函数计算了与给定的alpha设置相对应的t统计值。 |
| 8. | The distributions studied are normal distribution , student - t distribution , skewed student - t distribution and general error distribution . besides this , considering the conditional heteroskedasticity of the time serial in financial market , apply the garch model into the estimation of var
在此基础上，研究了证券市场上时间序列收益率波动的条件异方差性，考虑中国证券市场的风险特征，将garch系列模型与var模型相结合，构造了基于不同分布条件下的var模型。 |
| 9. | ( 3 ) how to design the bayesian test method about the parameter ' s linear hypothesis according to the relationship between the multivariate t distribution and f distribution . ( 4 ) the bayesian diagnosis and unit root test method about the random error series . ( 5 ) the bayesian mean value quality control chart when the variance is known and the mean value - standard error control chart when the variance is unknown
然后，研究了扩散先验分布下单方程模型参数的贝叶斯估计理论，证明了模型系数的后验分布为多元t分布，模型误差项方差的后验估计为逆gamma分布；根据多元t分布和f分布之间的关系，构造了模型系数线性假设检验的贝叶斯方法；根据hpd置信区间构造了随机误差序列自相关的贝叶斯诊断和单位根检验方法，并利用单方程模型的贝叶斯推断理论研究了方差已知时的贝叶斯均值控制图和方差未知时的贝叶斯均值?标准差控制图。 |
| 10. | ( 1 ) the posterior distribution of the coefficient matrix , the precision matrix and covariance matrix , and their bayesian estimation under the matrix normal - wishart conjugate prior distribution . ( 2 ) the deduction of the predictive distribution , proved to be matrix t distribution . ( 3 ) the designs of bayesian multivariate mean value control charts in terms of the relationship between the multivariate wishart distribution and x2 distribution , the bayesian process capability index and its confidence lower limi
通过多方程模型系统的统计结构，证明了矩阵正态? wishart先验分布是模型参数( ， )的共轭先验分布，研究了该先验分布下模型系数矩阵、精度阵和协方差阵的后验分布及其贝叶斯估计，对模型预报密度函数进行了严格的数学推导，并将其应用于多元质量控制领域，构造了贝叶斯均值向量联合控制图；结合wishart分布与x ~ 2分布之间的关系，设计与推断了贝叶斯多指标过程能力指数及其贝叶斯置信下限。 |
