核密度估計 的英文怎麼說

中文拼音 [hémìdùgūjì]
核密度估計 英文
kernal density
  • 核 : 核構詞成分。
  • 密 : Ⅰ名詞1 （秘密） secret 2 [紡織] （密度） density 3 （姓氏） a surname Ⅱ形容詞1 （距離近; 空隙小）...
  • 度 : 度動詞[書面語] （推測; 估計） surmise; estimate
  • 估 : 估構詞成分。
  • 計 : Ⅰ動詞1 （計算） count; compute; calculate; number 2 （設想; 打算） plan; plot Ⅱ名詞1 （測量或計算...
  • 估計 : estimate; evaluate; take stock of; size up; calculate; appraise; reckon; estimation; forecast
  1. Sequence and large sample quality are posed. in our country or allien there are many experts who discusses some quanlity of na variable in great. for example, in documentation 6, there is a discussion about the consistency of density kernel estimation and many results have been g ained, but the density estimation f ( x ) is limited to the arrange of x [ a, 6 ] which limits the arrange of application. thus in this passage we call off this limitation and further discuss the density kernel estimation under na sample. consequently the counterpart results have been gained and the condition of result has been weaked. in the same time we discuss the density kernel estimation under pa sample and the counterpart result has been gained

    序列密度函數估計及其大樣本性質的研究課題。國內外許多專家對na變量的各種性質進行了大量的討論，其中文獻[ 6 ]曾對na樣本下密度核估計的相合性進行了討論，並取得了一些結果，但該文將密度估計f （ x ）限制在x [ a ， b ]中進行討論，這局限了應用范圍。因此本文在取消這種限制條件下對na樣本的密度核估計進行了進一步的討論，得出了相應的結果，並使結論的條件得到弱化；同時對pa樣本下的密度估計也進行了相應的討論，取得了相應的結果。
  2. If the covariance stationary processes are one dimension, for given data, covariance function and spectral density function can be estimated, and there is no need to select kernel function and its parameters

    如果協方差平穩隨機過程的狀態是一維的，對給定的樣本點，給出了協方差函數的估計和其對應譜（密度）函數估計，而不必選擇核函數及其參數。
  3. Because the return rate has many good characters such as the following, we estimate the return rate by use of the characters. theorem 1 [ 20 ] set kernel function k ( u ) and density function f ( x ) satisfy the following conditions

    第三部分研究在收益率不服從正態分佈的情況下用核估計方法對股票的收益率密度做出估計，然後算出在期滿日時的股價，再用非參數估計方法對歐式看漲期權價值進行評估
  4. In order to obtain these data, we must at first calculate apsds ( auto - power spectral density ) and cpsds ( cross - power spectral density ) of the three signals in the system

    而想要得到這些核心參數，必須先分析估計出系統中三路信號的自功率譜密度和兩兩之間的互功率譜密度。
  5. This paper studies mainly the theories of the semi - parametric regression model : ( 1 ) under proper conditions, using random weighted way to the estimator of the error density f ( x ) of the semi - parametric regression model, this paper proved the strong and weak consistent and the asymptotic unbiased property of the weighted kernel estimation fn1 ( x ) of the f ( x )

    本文對半參數回歸模型：主要做了以下三個方面的理論研究： （ 1 ）將隨機加權法應用到半參數回歸模型的誤差密度f （ x ）的估計當中去，在適當的條件下，證明了誤差密度的加權核估計（ ? ） _ （ n1 ） （ x ）的強相合性、弱相合性及漸近無偏性。
  6. Moderate deviations for the kernel density estimators

    核密度估計的中偏差
  7. Methods non - parameter kernel density estimation method was adopted

    方法採用非參數核密度估計推斷方法。
  8. Compared with traditional pca - based methods which assumes that the data are independent and follow normal distribution, this kernel - density based method does n ' t require any assumption on data distribution

    而傳統的基於主元分析的系統性能監控方法都是假定測量數據相互獨立且服從正態分佈的，採用核密度估計方法無需對數據的分佈作任何假定。
  9. In last chapter, a new conception and model for var, based on prediction are brought forward. finally, a kind of new kernel density estimating function, adapting to financial time series is employed to extend time series kernel density estimating model

    文中最後一部分，從風險價值預測的角度出發，建立了基於var預測的概念和模型，提出了一種適合估計金融時間序列分佈的核密度函數，並採用加權法推廣了時間序列核密度估計模型
  10. Considering the fact that continuous process and batch process are the two important production modes in process industry, and each of them has its respective characteristic, our works are divided into two parts, those are, monitoring of continuous processes and of batch processes. the main contribution of this thesis is as follows, 1 multivariate kernel - density estimation method is used to calculate the distribution of data and assess the impact of parametric uncertainty on the monitoring performance

    由於連續生產方式和間歇生產方式是流程工業中兩種重要的生產方式，它們具有各自不同的特點，因此，本文的工作分兩大部分，即連續工業過程的監控和間歇工業過程的監控，具體包括： （ 1 ）採用多變量核密度估計方法，研究了參數不確定條件下，過程數據的分佈及其對系統監控性能的影響。
  11. It also puts forward logical explanations to a few facts that are still unable to be explained, up to now, by the classical consumption models. however, for the scholars in china, the cognition of the buffer - stock theory still remains at a superficial level of only providing simple theoretical introduction to it. a finer depiction and empirical application of it will not only facilitate studies on the related theories in china but also do great benefit to the development of the buffer - stock theory itself because china is admittedly one of the

    本論文的主要發現及創新包括:在理解和刻畫我國居民的預防性儲蓄行為時，緩沖儲備模型要優于以儲蓄水平量作為被解釋變量的模型;核密度估計方法對不確定性的描述更準確;從儲蓄率變化率的角度進行的格蘭傑因果關系檢驗證實了我國的居民存款儲蓄對經濟增長有推動作用;養老金的變化對我國居民儲蓄行為具有顯著影響，而我國現有的醫療保障體系沒有發揮其應該能夠發揮出的作用。
  12. Consistency of the density kernel estimator for negatively and positively associated samples

    樣本下密度核估計的相合性
  13. The result indicated that the wavelet estimator better than the kernel estimator in some conditions

    其計算實例結果表明在樣本量較大時，密度函數的小波估計較核估計好。
  14. In this dissertation, the research trends for the problem have been introduced ; the ‘ dim ’ and ‘ point ’ has been strictly defined in mathematics from machine vision and human vision ; the ideal clutter suppression system based on clutter predication and the realization and evaluation of evaluation index has been studied, in succession the clutter suppression technologies have been researched. firstly, the classic nonparametric algorithm has been analyzed in detail and systematically, for it ’ s weakness that it cannot remove the non - stationary clutter ideally, kalman filter algorithm for clutter suppression in 2d image signal has been built. secondly, fast adaptive kalman filter is presented based on fast wide - sense stationary areas partition algorithm : limited combination and division algorithm based on quarti - tree algorithm, new taxis filter route algorithm which can break through the limitation of the necessity of pixel neighborhood of 2d filter and laplace data model with two parameters which is perfectly suitable for the residual image of kalman clutter suppression

    首先分析了經典的非參數法，對於四種具有代表性的核，從前述的三個性能評價方面做了分析和對比，指出了其速度快的優點和對非平穩圖像適應性差的弱點，針對非參數法的弱點，重點研究了對非平穩圖像適應良好的卡爾曼雜波抑制技術:建立了非平穩圖像的類自回歸模型，在此基礎上建立了二維卡爾曼濾波基礎的兩個方程:狀態方程和測量方程;建立了非平穩圖像準平穩區域快速劃分演算法:基於四叉樹法的有限分裂合併演算法;二維空間的基於k排序的濾波路線演算法，突破了空域濾波路線上區域相鄰的限制;在這些研究的基礎上實現了快速卡爾曼估計，實驗驗證了該方法相對逐點卡爾曼估計可以提高運算速度三倍左右;雜波抑制結果表明傳統的高斯性檢驗並不適合卡爾曼估計后的殘余圖像，由此建立了殘余圖像的雙參數拉普拉斯模型，實驗表明其可以完好的吻合殘余圖像的概率密度曲線。
  15. In recent years various methods of estimation about probability density function of random variable sequence that is independent and identically distributed ( abbreviated as iid. ) and large sample quality have been more discussed in research documentation

    ）序列的概率密度函數的各種估計方法及其大樣本性質的研究文獻中已有很多討論，研究得最多的還是密度函數的核估計，如rosenblent ， parzen ， prakasarao和silverman等。
  16. This paper investigates the application of the multivariate statistical process monitoring and control technology, which employs both multiway principal component analysis ( mpca ) and kernel density estimation ( kde ), to real time status monitoring and fault diagnosis of batch production processes

    本文主要研究了運用多向主元分析法和核函數法概率密度估計相結合的多元統計過程監控技術對間歇生產過程進行實時的狀態監測與故障診斷。
  17. Kde is a non - parametric method which is capable of extracting the population ' s probability density function ( pdf ) based on data sample only without any a prior knowledge about the statistic properties of the data regime. in this thesis, it is conducted the implementation of the kde for monitoring the performance of batch production processes

    用核函數法概率密度估計對間歇生產過程進行實時狀態監測的主要優點是它屬于非參數法概率密度估計的一種，不需要數據總體的任何先驗知識或是假設而直接基於實測數據樣本求出總體的概率分佈密度函數，擺脫了對不可靠的先驗知識的依賴。
  18. Under the frame of natural gradient algorithm, an ica algorithm based on adaptive kernel estimation is proposed, which can separate arbitrary mixed signals ( such as super - gaussian and sub - gaussian, symmetric and asymmetric signals )

    摘要在自然梯度演算法的框架下，本文利用隨機變量概率密度函數非參數估計的自適應核函數法，給出了一種能夠對任意混合信號（超高斯和亞高斯信號，對稱和非對稱分佈信號）進行盲分離的演算法。
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