極小方差估計 的英文怎麼說

中文拼音 [jíxiǎofāngchāgūjì]
極小方差估計 英文
minimum variance estimate
  • 極 : i 名詞1 （頂點; 盡頭） the utmost point; extreme 2 （地球的南北兩端; 磁體的兩端; 電源或電器上電流...
  • 小 : Ⅰ形容詞1 （體積、面積、數量、強度等不大） small; little; petty; minor 2 （年紀小的; 年幼的） youn...
  • 方 : Ⅰ名詞1 （方形; 方體） square 2 [數學] （乘方） involution; power 3 （方向） direction 4 （方面） ...
  • 差 : 差Ⅰ名詞1 （不相同; 不相合） difference; dissimilarity 2 （差錯） mistake 3 [數學] （差數） differ...
  • 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. A batch least - squares maximum likelihood estimator is employed to calibrate the model coefficients of accelerometer and a polynomial post - fit method is used to establish temperature models of these coefficients. the temperature models of accelerometer bias and scale factor of accelerometer are established between - 20oc and 50 oc. after compensating the temperature error by using these models, the post - fit residuals of the accelerometer output have been improved to 10 ? 5 g, and the trend term of accelerometer changing with temperature basically vanished

    採用最小二乘極大似然估計和多項式擬合的方法，分析加速度計靜態模型系數隨加速度計殼體溫度變化的規律，建立了- 20oc 50oc之間加速度計零偏和標度因數誤差的溫度模型，應用該模型對加速度計溫度干擾進行補償，補償后，加速度計輸出的擬合均方根誤差一到二個數量級，並且基本上消除了加速度計輸出隨溫度變化的趨勢項，使得加速度計測量精度得到了明顯提高。
  2. It comes up with a new notion, d - solution, which is applied to the distance estimation, by virtue of hilbert space ; furthermore, the dissertation has gained a necessary condition which is identity of minimum mean - square value in linear function classes, so that d - solution extends minimum mean - square value within the domain of nonlinear function equation or equation system ; and, the dissertation studies in detail the classical moment estimation and maximal likelihood estimation on the parameters of ar ( p ), a series of theorems in the estimation section shows the moment estimators are consistent on the ground of large samples jikewise, those distribution functions of the estimated parameters accord to maximum likelihood estimation converge gauss distribution if the white noise is gaussan

    首先，藉助hilbert空間理論，提出了距離估計的d -解，給出了d -解的必要條件，這個條件在線性函數類里即是極小二乘估計法， d -解的必要條件滿足的方程實質上將極小二乘估計法推廣到多函數及非線性函數類。再而，詳細地研究了多元弱平穩序列自回歸模型ar （ p ）的參數經典的矩的替代估計和極大似然估計，獲得矩的替代估計的一致性的結果。對基於gauss白噪聲假設多元弱平穩序列自回歸模型的均值、白噪聲的協方差陣的極大似然估計都有依分佈收斂到多元正態分佈的統計性質。
  3. In this paper, we discussed the procedures of quantiles, maximum - likelihood, probability weighted moments, moments, least square, the best linear unbiased estimate, good linear unbiased estimation, and the best invariant estimate to the parameters of gumbel distribution, then give out the expectation and variance - covariance respectively. we compared the statistical behavior of these eight estimate procedures not only theoretically but also in the monte - carlo simulation

    本文利用分位數法、極大似然法、概率加權矩法、矩法、最小二乘法、最佳線性無偏估計法、簡單線性無偏估計法、最好線性同變估計法對gumbel分佈中的參數進行估計，分別給出了這八種估計量的期望、方差和協方差。
  4. In special ly, the est imat ion of power spectra1 densi ty ( psd ) can be get in burg method which estimate reflection coefficient firstly, then get the ar parameter est imat ion usi ng the levinson recursion

    用遞推方法對不同階的預測是通過使預測誤差功率的估計值達到極小，從而得到反射系數估計值的，最終得到功率譜估計值。
  5. ( 3 ) aiming at the deficiency of present methods of network traffic anomaly, this paper proposes a new scale - adaptive detection mechanism. by means of wavelet packet decomposition, our method has the same detective ability to middle and high frequency as well as low frequency anomaly ; by means of reconstruct the wavelet packet

    該方法利用層疊模型從本質上體現流量性質的特點，選擇用多分辨分析（ mra ）和小波變換模極大（ wtmm ）估計層疊模型的判定條件作為檢測依據，通過對擬合曲線誤差的估計來判定異常的發生。
  6. Firstly, we estimate the variance and the mean of each cell with maximum likelihood ; secondly, we identify the important dispersion effects based on least squares analysis of the logarithm of within - replication variance ; last, we identify the important location effects based on weighted least squares analysis of the mean of each cell. a simulation study also demonstrates its superiority over some existing methods. an experiment for the robust design of thermostat is used to illustrate the method

    本文對帶有右截尾數據的有重復因子試驗，提出了另一種分析位置效應和散度效應的方法：首先，在每一個試驗點，對重復試驗觀察值用極大似然法估計出均值和方差；其次，用每個試驗點方差估計值的對數作為響應變量與各因子建立回歸模型，鑒別出顯著的散度效應；之後，採用加權最小二乘法鑒別出比較顯著的位置效應。
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