2 edition of **Large sample efficiencies of invariant quadratic unbiased estimators** found in the catalog.

Large sample efficiencies of invariant quadratic unbiased estimators

Neil Kenneth Poulsen

- 239 Want to read
- 22 Currently reading

Published
**1982**
.

Written in English

- Estimation theory.

**Edition Notes**

Statement | by Neil Kenneth Poulsen. |

The Physical Object | |
---|---|

Pagination | [6], 78 leaves, bound : |

Number of Pages | 78 |

ID Numbers | |

Open Library | OL14219284M |

Battese (). Maximum Likelihood Estimation (MLE) under the normality of the disturbances is derived by Amemiya (). The first-order conditions are non-linear, but can be solved using an iterative GLS scheme, see Breusch (). Finally one can apply Rao's () Minimum Norm Quadratic Unbiased Estimation (MINQUE) methods. If the estimator µ^ is unbiased, then the MSE equals the Var(^µ). So having an unbiased estimator may reduce the MSE. However this is not necessary and one should be willing to accept a (small) bias, as long as the MSE becomes smaller. The sample mean is an unbiased estimator. Moreover it is immediate (why?) that Var(^„) = ¾2.

Minimum norm quadratic unbiased estimator (MINQUE) is proposed as a method of variance estimation in a series of papers starting with Rao (). The basic idea for this method is to find unbiased quadratic estimators that are invariant, and to minimize some matrix norms. One of the major disadvantages associated with the MINQUE estimator is that. x=[, , , , , , ]; m=mean(x); v=var(x); s=std(x);.

Background. An "estimator" or "point estimate" is a statistic (that is, a function of the data) that is used to infer the value of an unknown parameter in a statistical parameter being estimated is sometimes called the can be either finite-dimensional (in parametric and semi-parametric models), or infinite-dimensional (semi-parametric and non-parametric models). In statistics, the concept of being an invariant estimator is a criterion that can be used to compare the properties of different estimators for the same quantity. It is a way of formalising the idea that an estimator should have certain intuitively appealing qualities. Strictly speaking, "invariant" would mean that the estimates themselves are unchanged when both the measurements and the.

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The set of quadratic unbiased estimators considered includes the minimal complete class. A theorem is proved which shows that, in certain cases, a relatively simple expression converges to the same value to which the efficiency itself converges. The efficiency is a much more complex : Neil Kenneth Poulsen.

The MIVQUE estimates have minimum variance within the class of invariant quadratic unbiased estimators under normality, but it is demonstrated that other commonly used estimates may be more efficient in nonnormal situations.

The potential gain in efficiency, however, is often small relative to the potential loss of efficiency relative to by: Large sample efficiencies of invariant quadratic unbiased estimators.

Abstract. Graduation date: This dissertation examines limiting efficiencies of\ud quadratic unbiased estimators for the variance in the\ud two variance component mixed model. The set of\ud quadratic unbiased estimators considered includes the\ud minimal complete class.

Title: LARGE SAMPLE EFFICIENCIES OF INVARIANT QUADRATIC UNBIASED ESTIMATORS Redacted for privacy Abstract approved Justus F.

Seely / This dissertation examines limiting efficiencies of quadratic unbiased estimators for the variance in the.

In their recent paper Olsen, Seely and Birkes [8] have given a characterization of the class of admissible invariant, unbiased and quadratic estimators for two variance components in a mixed linear model. In this paper our goal is to characterize the class of admissible, invariant and unbiased estimators for any number of variance by: 1.

Wu, Zou and Chen [24] investigated unbiased invariant minimum norm and uniformly minimum variance nonnegative quadratic unbiased estimators for a linear parameter function tr(CΣ) of Σ.

estimating the variance of a sample mean; see Song and Schmeiser (). The multi-taper estimator of a spectral density (e.g. Percival and Walden (, Ch 7)) also belongs to the class of quadratic estimators. Some long run variance estimators in the econometrics literature can be written as quadratic estimators (see Sun ()).

Therefore. Abstract. The following question is addressed: For which quadratic unbiased estimates of variance components, and under what asymptotic assumptions, are the estimates as efficient as estimates based on the random effects themselves, with or without the normality assumption?Westfall and Bremer () have identified sufficient asymptotic conditions under which such an efficiency property holds.

Optimal Unbiased Estimation In the last lecture, we introduced three techniques for nding optimal unbiased estimators Now consider the quadratic form q() = 2Var(U)+2 Cov(0;U).

The form qhas the roots 0 and 2Cov( invariant if f +c(x+ c) = f (x). De nition 3. (Location invariant loss function). A loss function Lis location invariant. ESTIMATION WITH QUADRATIC LOSS covariance matrixequalto theidentity matrix, that is, E(X-t)(X-t)I. Weareinterested inestimatingt, sayby4anddefinethelossto be (1) LQ(, 4) = (t-) = |-J, using the notation (2)-1X =x'x.

Theusualestimatoris 'po, definedby (3) OW(x) =x, andits risk is (4) p(Q, po) =EL[t, po(X)] =E(X -t)'(X-= p. It is well knownthat amongall unbiased estimators, or amongall.

It has been shown that the best among these linear invariant estimators can be calculated as a function of the best linear unbiased (BLU) estimators of u and b and their covariance matrix.

Moreover, the expected loss of any best linear invariant (BLI) estimator is uniformly less than that of the corresponding BLU estimator. This chapter introduces biased and unbiased estimators—for example, sample variance is an unbiased estimator of the population variance, while the sample standard deviation is a biased estimator.

The principle of maximum likelihood provides a unified approach to estimating parameters of the distribution given sample data. Although ML estimators $\hat{\theta}_n$ are not in general unbiased, they possess a number of desirable asymptotic properties. consistency: $\hat{\theta}_n \stackrel{n \to \infty}{\to} \theta$; normality: $ \hat{\theta}_n \sim \mathcal{N}(\theta, \Sigma)$, where.

Unbiased functions More generally t(X) is unbiased for a function g(θ) if E θ{t(X)} = g(θ). Note that even if θˆ is an unbiased estimator of θ, g(θˆ) will generally not be an unbiased estimator of g(θ) unless g is linear or aﬃne. This limits the importance of the notion of unbiasedness.

It. J.D. Rope, Best nonnegative invariant partially orthogonal quadratic estimation in normal regression, J. Amer. Statist. Assoc. () [9] J.D. Rolle, Optimization of functions of matrices with application in statistics and econometrics, Linear. Asymptotic Efficiency • We compare two sample statistics in terms of their variances.

The statistic with the smallest variance is called. efficient. • When we look at asymptotic efficiency, we look at the asymptotic variance of two statistics as.

grows. Note that if we compare two consistent estimators, both variances eventually go to. Note that being unbiased is a precondition for an estima-tor to be consistent.

Example 1: The variance of the sample mean X¯ is σ2/n, which decreases to zero as we increase the sample size n. Hence, the sample mean is a consistent estimator for µ. Example 2: The variance of the average of two randomly.

Estimation with Quadratic Loss. Published on Jan 1, DOI: / A proof that the best unbiased estimator of a linear function of the means of a set of observed random variables is the least squares estimator was given by Markov [12], a modified version of whose proof is given by David and Neyman [4].

Testing Goodness of. whereas the formula to estimate the variance from a sample is Notice that the denominators of the formulas are different: N for the population and N-1 for the sample.

We saw in the " Estimating Variance Simulation " that if N is used in the formula for s 2, then the estimates tend to. Bayes and best quadratic unbiased estimators for parameters of the covariance matrix in a normal linear model.

Math. Operationsf. Statist. 5, LaMotte, L.R. Quadratic estimation of variance components. Biometr LaMotte, L.R.

Invariant quadratic estimators in the random one-way ANOVA model. Biometr. The best S2-based quadratic unbiased estimator is presented explicitly. The Cramer-Rao upper bound for the efficiency of unbiased estimators, corresponding to the efficiency of large-sample Maximum Likelihood estimators, is This bound cannot be attained because the distribution is not of exponential type.Introduction to Statistical Methodology Unbiased Estimation The last line uses (3).

This shows that S 2is a biased estimator for ˙. Using the deﬁnition in (2), we can see that it is biased downwards. b(˙2) = n 1 n ˙2 ˙2 = 1 n ˙2: In addition, E n n 1 S2 = ˙2 and S2 u = n n 1 S2 = 1 n 1 Xn i=1 (X i X)2 is an unbiased estimator for ˙2.The ﬁnite-sample properties of the least squares estimator are independent of the sample size.

The linear model is one of relatively few settings in which deﬁnite statements can be made about the exact ﬁnite-sample properties of any estimator.

In most cases, the only known properties are those that apply to large samples.