항목
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M-estimatorIn statistics, M-estimators are a broad class of estimators, which are obtained as the minima of sums of functions of the data. Least-squares estimators are M-estimators. The definition of M-estimators was motivated by robust statistics, which contributed new types of M-estimators. The statistica...출처 영어 위키백과
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Redescending M-estimatorsmoothly to 0. This means that moderately large outliers are not ignored completely, and greatly improves the efficiency of the redescending M-estimator.The redescending M-estimators are slightly more efficient than the Huber estimator for several symmetric, wider tailed distributions, but about...출처 영어 위키백과
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K-T 추정량 K-T 추정량, Krichevsky–Trofimov estimatorK-T 추정량(Krichevsky–Trofimov estimator)이란 정보이론에서, A의 알파벳을 갖는 stationary source pi가 주어졌을 때, symbol i \in A를 뽑을 확률 \pi_i(w...1}일 때에 m개의 0과 n개의 1이 주어졌다면 K-T 추정량 P &= 1, \\ P(m, n+1) &= P(m,n)\dfrac{n + 1/2}{m + n + 1}, \\ P(m+1, n) &= P(m,n)\dfrac{m + 1...도서 위키백과
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L-estimatorwith increasing n and the scale factor can be improved (efficiency 85% for 10 points). Other heuristic estimators for small samples include the range over n (for standard error), and the range squared over the median (for the chi-squared of a Poisson distribution). L-moment M-estimator– sec. 5.2.2출처 영어 위키백과
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Multi-fractional order estimatortarget trajectory. The MFOE overcomes the long-ago rejection of terms higher than 3rd order because, taken at full value (i.e., f_{m}s=1), estimator variances increase exponentially with linear order increases. (This is elucidated below in the section "Application of the FOE".) As described in...출처 영어 위키백과
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Minimax estimatorcriteria for finding an optimal estimator in some sense are required. One such criterion is the minimax criteria. Definition Definition : An estimator \delta^M:\mathcal{X} \rightarrow \Theta \,\! is called minimax with respect to a risk function R(\theta,\delta) \,\! if it achieves the smallest...출처 영어 위키백과
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Ratio estimatorin operational research. Industrielle Organization 31: 27-28 r_\mathrm{ corr } = r \frac{ 1 + \theta c_{ xy } }{ 1 + \theta c_x^2 } Tin's estimator:Tin M (1965) Comparison of some ratio estimators. JASA 60: 294–307 r_\mathrm{ corr } = r \left( 1 + \theta \left( c_{ xy } - c_x^2 \right) \right...출처 영어 위키백과
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James–Stein estimatorStein estimator always achieves lower MSE than the maximum likelihood estimator. By definition, this makes the least squares estimator inadmissible when m \ge 3.Notice that if (m-2) \sigma^2<\|{\mathbf y}\|^2 then this estimator simply takes the natural estimator \mathbf y and shrinks it towards...출처 영어 위키백과
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Leonard–Merritt mass estimatorentirely of luminous stars (i.e. no dark matter or black holes).In a cluster with constant mass-to-light ratio and total mass M_T, the Leonard–Merritt estimator becomes:M_T = {32\over 3\pi G} \langle R \left(2V_R^2 + V_T^2\right)\rangle.On the other hand, if all the mass is located in a central...출처 영어 위키백과
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Bayes estimatorwidehat{\sigma}_\pi^{2}=\widehat{\sigma}_m^{2}-K. For example, if x_i|\theta_i \sim N(\theta_i,1), and if we assume a normal prior (which is a...in this case), we conclude that \theta_{n+1}\sim N(\widehat{\mu}_\pi,\widehat{\sigma}_\pi^{2}) , from which the Bayes estimator of \theta_{n+1} based on...출처 영어 위키백과
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Hodges–Lehmann estimator ホッジス・レーマン推定量points (one from each set); each such pair defines one difference of values. The Hodges–Lehmann statistic is the median of the m × n differences.Everitt (2002) Entry for "Hodges-Lehmann estimator" For a population that is symmetric, the Hodges–Lehmann statistic estimates the population's...출처 영어 위키백과
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Krichevsky–Trofimov estimatorThis estimator is optimal in the sense that it minimizes the worst-case regret asymptotically.For a binary alphabet, and a string w with m zeroes and n ones, the KT estimator can be defined recursively as: \begin{array}{lcl} P(0, 0) & = & 1, \\ [6pt] P(m, n+1) & = & P(m,n)\dfrac{n + 1/2}{m + n...출처 영어 위키백과