跳到正文
收录说明
收录范围
保留计量方法研究、相关统计基础,以及实质涉及计量方法的应用、教程、比较和综述。书评、刊务及明确无关内容不公开;身份或相关性证据不足的记录保留待核,补充可靠材料后可重新判断。
当前期刊来源见下方官网链接及期刊目录。综合经济学与金融期刊逐篇筛查计量方向,证据不足时暂不公开。正刊、在线优先论文和已核实接收稿合并展示;按标题、可靠摘要、目录及身份匹配的出版社 JEL 分类核验相关性;计量类 JEL 与摘要相互印证,无 JEL 不直接排除。历史回补以可取得的注册题录和目录为准,不等于覆盖全部纸本。
日期与排序
默认按最近发表排序:正刊使用刊期,待刊使用上线日期;缺少上线日期时,排序参考登记或首次收录时间,并在卡片注明。更多筛选可选择出版状态和日期口径;卷期依据出版社元数据,不因待刊时间较长而推定已编入正刊。仅有月份或年份时,日期区间相交也会纳入。

日期精度:到日 3 篇 · 到月 5 篇 · 到年 4 篇

分类与引用
领域和方法依据题名、摘要初步分类,可人工修正;分类不代表论文质量。引用数采用 OpenAlex 单条关联记录,不合计版本。
更新与 AI 解读
正常题录、可靠摘要与引用同步继续;日、周、月简报按核实的发表与修订日期汇总,历史补录保留在文献库。AI 解读依据可靠摘要或已核实原文片段,已有解读保留供阅读参考;缺少可靠材料的条目保持待解读,浏览页面不会触发生成。
12 篇论文
导出

Monotone Gain, First-Order Autocorrelation and Zero-Crossing Rate

单调增益、一阶自相关与过零率

The effect of a linear filter with monotone gain on the first-order autocorrelation of a weakly stationary time series is discussed. When the gain is monotone increasing, the first-order autocorrelation cannot increase. Otherwise, when the gain is monotone decreasing, the correlation cannot decrease. Further, when the gain is strictly monotone, the first-order autocorrelation is unchanged if and only if the process is a pure sinusoid with probability 1. Under the Gaussian…

AI 解读阅读原文 ↗BibTeX ↓

On the Asymptotic Variance of Higher Order Crossings with Special Reference to a Fast White Noise Test

高阶穿越的渐近方差及其在快速白噪声检验中的应用

The rate of decrease in the variance of higher order crossings is obtained for stationary m-dependent Gaussian sequences. This result provides a quick approximation to the variance of the number of axis-crossings of m-dependent processes, and is applied in determining approximate probability limits for the higher order crossings of white noise. The limits are used in testing for white noise in simulated and also real data examples.

AI 解读阅读原文 ↗BibTeX ↓

On the asymptotic variance of higher order crossings with special reference to a fast white noise test

高阶交叉的渐近方差及其快速白噪声检验应用

The rate of decrease in the variance of higher order crossings is obtained for stationary m-dependent Gaussian sequences. This result provides a quick approximation to the variance of the number of axis-crossings of m-dependent processes, and is applied in determining approximate probability limits for the higher order crossings of white noise. The limits are used in testing for white noise in simulated and also real data examples.

AI 解读阅读原文 ↗BibTeX ↓

Time Series Discrimination by Higher Order Crossings

利用高阶过轴次数辨别时间序列

A new methodology is proposed for discrimination among stationary time-series. The time series are transformed into binary arrays by clipping (retaining only the signs of) the $j$th difference series, $j = 0, 1, 2, \cdots$. The degeneracy of clipped $j$th differences is studied as $j$ becomes large. A new goodness of fit statistic is defined as a quadratic form in the counts of axis-crossings by each of the first $k$ differences of the series. Simulations and the degeneracy…

AI 解读阅读原文 ↗BibTeX ↓

On Goodness of Fit of Time Series Models: An Application of Higher Order Crossings

时间序列模型的拟合优度:高阶穿越的应用

A relation between the graphical appearance of a stationary time series and its autocorrelations is discussed. The use of counts of visual features termed higher order crossings is proposed in testing goodness of fit of hypothesized models against very general alternatives. Attention is focused on a specific example of a new type of nonparametric test statistic arising from iterative feature extraction procedures called extractors. The statistic is compared with Box &…

AI 解读阅读原文 ↗BibTeX ↓

On goodness of fit of time series models: An application of higher order crossings

时间序列模型拟合优度:高阶交叉的应用

A relation between the graphical appearance of a stationary time series and its autocorrelations is discussed. The use of counts of visual features termed higher order crossings is proposed in testing goodness of fit of hypothesized models against very general alternatives. Attention is focused on a specific example of a new type of nonparametric test statistic arising from iterative feature extraction procedures called extractors. The statistic is compared with Box &…

AI 解读阅读原文 ↗BibTeX ↓

Estimation of the Parameters in Stationary Autoregressive Processes after Hard Limiting

硬限幅后平稳自回归过程的参数估计

Estimates of the parameters in normal autoregressive (AR(p)) processes may be obtained as functions of certain runs and subsequences in the associated clipped 0 − 1 processes. For example, the parameter in AR (1) is a function of the number of 1 runs only. Equivalently, this parameter can be estimated by counting only the number of axis crossings by the process. The estimates are obtained by a modification of the likelihood function of the clipped data. The loss of…

AI 解读阅读原文 ↗BibTeX ↓

Sufficiency and the Number of Level Crossings by a Stationary Process

充分性与平稳过程的水平穿越次数

It is shown how to derive the exact distribution of the number of axis crossings by a stationary process when the binary process obtained by clipping the original process is a pth-order Markov chain. The same method is used in deriving the asymptotic distribution of the number of upcrossings of a high level by a stationary process.

AI 解读阅读原文 ↗BibTeX ↓

Sufficiency and the number of level crossings by a stationary process

平稳过程的充分性与水平穿越次数

It is shown how to derive the exact distribution of the number of axis crossings by a stationary process when the binary process obtained by clipping the original process is a pth-order Markov chain. The same method is used in deriving the asymptotic distribution of the number of upcrossings of a high level by a stationary process.

AI 解读阅读原文 ↗BibTeX ↓

Sufficient Statistics Associated with a Two-State Second-Order Markov Chain

两状态二阶马尔可夫链的充分统计量

Statistical inference for binomial processes with second-order Markov dependence is worked out. Sufficient statistics for the 2 2 parameters associated with the process are found and their exact joint distribution is given in a closed form. An algorithm for finding the probability of k successes in n dependent trials is developed and a simple estimation procedure is suggested and applied to a meteorological time series.

AI 解读阅读原文 ↗BibTeX ↓

Sufficient statistics associated with a two-state second-order Markov chain

两状态二阶马尔可夫链的充分统计量

Statistical inference for binomial processes with second-order Markov dependence is worked out. Sufficient statistics for the 2 2 parameters associated with the process are found and their exact joint distribution is given in a closed form. An algorithm for finding the probability of k successes in n dependent trials is developed and a simple estimation procedure is suggested and applied to a meteorological time series.

AI 解读阅读原文 ↗BibTeX ↓