Relevant bibliographies by topics / Optimal estimator / Journal articles

To see the other types of publications on this topic, follow the link: Optimal estimator.

Journal articles on the topic 'Optimal estimator'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Optimal estimator.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

Lui, Kung-Jong. "Notes on Use of the Composite Estimator: an Improvement of the Ratio Estimator." Journal of Official Statistics 36, no. 1 (2020): 137–49. http://dx.doi.org/10.2478/jos-2020-0007.

Full text

Abstract:

AbstractThis article discusses use of the composite estimator with the optimal weight to reduce the variance (or the mean-squared-error, MSE) of the ratio estimator. To study the practical usefulness of the proposed composite estimator, a Monte Carlo simulation is performed comparing the bias and MSE of composite estimators (with estimated optimal weight and with known optimal weight) with those of the simple expansion and the ratio estimators. Two examples, one regarding the estimation of dead fir trees via an aerial photo and the other regarding the estimation of the average sugarcane acres per county, are included to illustrate the use of the composite estimator developed here.

APA, Harvard, Vancouver, ISO, and other styles

Cordue, Patrick L. "Designing optimal estimators for fish stock assessment." Canadian Journal of Fisheries and Aquatic Sciences 55, no. 2 (1998): 376–86. http://dx.doi.org/10.1139/f97-228.

Full text

Abstract:

Many estimation procedures are used in the provision of fisheries stock assessment advice. Most procedures use estimators that have optimal large-sample characteristics, but these are often applied to small-sample data sets. In this paper, a minimum integrated average expected loss (MIAEL) estimation procedure is presented. By its design a MIAEL estimator has optimal characteristics for the type of data it is applied to, given that the model assumptions of the particular problem are satisfied. The estimation procedure is developed within a decision-theoretic framework and illustrated with a Bernoulli and a fisheries example. MIAEL estimation is related to optimal Bayes estimation, as both procedures seek an estimator that minimizes an integrated loss function. In most fisheries applications a global MIAEL estimator will be difficult to determine, and a MIAEL estimator will need to be found within a given class of estimators. "Squared f-error," a generalization of the common squared error loss function is defined. It is shown that an estimator can be improved (for a given squared f-error loss function) by using its best linear transformation which is the MIAEL estimator within the class of linear transformations (in f space).

APA, Harvard, Vancouver, ISO, and other styles

Setiawan, Ezra Putranda, and Dedi Rosadi. "APPLICATION OF ROBUST REGRESSION FOR PORTFOLIO OPTIMIZATION." Matrix Science Mathematic 7, no. 1 (2023): 07–15. http://dx.doi.org/10.26480/msmk.01.2023.07.15.

Full text

Abstract:

The single-index model is a portfolio optimization method that uses each asset’s beta’. In general, the beta is estimated using the return data by the least square method. However, the return data frequently contains several outliers, so the estimation resulting from the least square method is inaccurate. This study examines several beta estimators from robust regression methods, namely the least absolute value estimator, M-estimator, LMS-estimator, LTS-estimator, MM-estimator, and Tau estimator to estimate the beta of each asset and make an optimal portfolio based on this estimated value. We also evaluate the effect of robust beta estimators on the stability and performance of each portfolio. We present the Sharpe ratio and some turnover measures, namely the l-period portfolio turnover, maximum turnover, lower bound single-asset turnover, and lower bound multiple-asset turnover. Among various estimators used here, the Tau estimator is the best estimator to replace the OLS for estimating the beta.

APA, Harvard, Vancouver, ISO, and other styles

Martínez, Sergio, María del Mar Rueda, and María Dolores Illescas. "The optimization problem of quantile and poverty measures estimation based on calibration." Journal of Computational and Applied Mathematics 405 (June 12, 2020): 113054. https://doi.org/10.5281/zenodo.10583622.

Full text

Abstract:

New calibrated estimators of quantiles and poverty measures are proposed. These estimators combine the incorporation of auxiliary information provided by auxiliary variables related to the variable of interest by calibration techniques with the selection of optimal calibration points under simple random sampling without replacement. The problem of selecting calibration points that minimize the asymptotic variance of the quantile estimator is addressed. Once the problem is solved, the definition of the new quantile estimator requires that the optimal estimator of the distribution function on which it is based verifies the properties of the distribution function. Through a theorem, the nondecreasing monotony property for the optimal estimator of the distribution function is established and the corresponding optimal estimator can be defined. This optimal quantile estimator is also used to define new estimators for poverty measures. Simulation studies with real data from the Spanish living conditions survey compares the performance of the new estimators against various methods proposed previously, where some resampling techniques are used for the variance estimation. Based on the results of the simulation study, the proposed estimators show a good performance and are a reasonable alternative to other estimators.

APA, Harvard, Vancouver, ISO, and other styles

Zhao, Quanshui. "ASYMPTOTICALLY EFFICIENT MEDIAN REGRESSION IN THE PRESENCE OF HETEROSKEDASTICITY OF UNKNOWN FORM." Econometric Theory 17, no. 4 (2001): 765–84. http://dx.doi.org/10.1017/s0266466601174050.

Full text

Abstract:

We consider a linear model with heteroskedasticity of unknown form. Using Stone's (1977, Annals of Statistics 5, 595–645) k nearest neighbors (k-NN) estimation approach, the optimal weightings for efficient least absolute deviation regression are estimated consistently using residuals from preliminary estimation. The reweighted least absolute deviation or median regression estimator with the estimated weights is shown to be equivalent to the estimator using the true but unknown weights under mild conditions. Asymptotic normality of the estimators is also established. In the finite sample case, the proposed estimators are found to outperform the generalized least squares method of Robinson (1987, Econometrica 55, 875–891) and the one-step estimator of Newey and Powell (1990, Econometric Theory 6, 295–317) based on a Monte Carlo simulation experiment.

APA, Harvard, Vancouver, ISO, and other styles

Zakerzadeh, Hojatollah, Ali Akbar Jafari, and Mahdieh Karimi. "Optimal Shrinkage Estimations for the Parameters of Exponential Distribution Based on Record Values." Revista Colombiana de Estadística 39, no. 1 (2016): 33–44. http://dx.doi.org/10.15446/rce.v39n1.55137.

Full text

Abstract:

This paper studies shrinkage estimation after the preliminary test for the parameters of exponential distribution based on record values. The optimal value of shrinkage coefficients is also obtained based on the minimax regret criterion. The maximum likelihood, pre-test, and shrinkage estimators are compared using a simulation study. The results to estimate the scale parameter show that the optimal shrinkage estimator is better than the maximum likelihood estimator in all cases, and when the prior guess is near the true value, the pre-test estimator is better than shrinkage estimator. The results to estimate the location parameter show that the optimal shrinkage estimator is better than maximum likelihood estimator when a prior guess is close to the true value. All estimators are illustrated by a numerical example.

APA, Harvard, Vancouver, ISO, and other styles

Lakhdar, Yissam, and El Hassan Sbai. "Online Variable Kernel Estimator." International Journal of Operations Research and Information Systems 8, no. 1 (2017): 58–92. http://dx.doi.org/10.4018/ijoris.2017010104.

Full text

Abstract:

In this work, the authors propose a novel method called online variable kernel estimation of the probability density function (pdf). This new online estimator combines the characteristics and properties of two estimators namely nearest neighbors estimator and the Parzen-Rosenblatt estimator. Their approach allows a compact online adaptation of the estimated probability density function from the new arrival data. The performance of the online variable kernel estimator (OVKE) depends on the choice of the bandwidth. The authors present in this article a new technique for determining the optimal smoothing parameter of OVKE based on the maximum entropy principle (MEP). The robustness and performance of the proposed approach are demonstrated by examples of online estimation of real and simulated data distributions.

APA, Harvard, Vancouver, ISO, and other styles

Hirukawa, Masayuki. "A TWO-STAGE PLUG-IN BANDWIDTH SELECTION AND ITS IMPLEMENTATION FOR COVARIANCE ESTIMATION." Econometric Theory 26, no. 3 (2009): 710–43. http://dx.doi.org/10.1017/s0266466609990089.

Full text

Abstract:

The two most popular bandwidth choice rules for kernel HAC estimation have been proposed by Andrews (1991) and Newey and West (1994). This paper suggests an alternative approach that estimates an unknown quantity in the optimal bandwidth for the HAC estimator (called normalized curvature) using a general class of kernels, and derives the optimal bandwidth that minimizes the asymptotic mean squared error of the estimator of normalized curvature. It is shown that the optimal bandwidth for the kernel-smoothed normalized curvature estimator should diverge at a slower rate than that of the HAC estimator using the same kernel. An implementation method of the optimal bandwidth for the HAC estimator, which is analogous to the one for probability density estimation by Sheather and Jones (1991), is also developed. The finite sample performance of the new bandwidth choice rule is assessed through Monte Carlo simulations.

APA, Harvard, Vancouver, ISO, and other styles

Pearce, Mark E., Earl T. Campbell, and Pieter Kok. "Optimal quantum metrology of distant black bodies." Quantum 1 (July 26, 2017): 21. http://dx.doi.org/10.22331/q-2017-07-26-21.

Full text

Abstract:

Measurements of an object's temperature are important in many disciplines, from astronomy to engineering, as are estimates of an object's spatial configuration. We present the quantum optimal estimator for the temperature of a distant body based on the black body radiation received in the far-field. We also show how to perform separable quantum optimal estimates of the spatial configuration of a distant object, i.e. imaging. In doing so we necessarily deal with multi-parameter quantum estimation of incompatible observables, a problem that is poorly understood. We compare our optimal observables to the two mode analogue of lensed imaging and find that the latter is far from optimal, even when compared to measurements which are separable. To prove the optimality of the estimators we show that they minimise the cost function weighted by the quantum Fisher information---this is equivalent to maximising the average fidelity between the actual state and the estimated one.

APA, Harvard, Vancouver, ISO, and other styles

Gonzalez, Luz Mery, Julio M. Singer, and Edward J. Stanek III. "Finite Population Mixed Models for Pretest-Posttest Designs with Response Errors." Revista Colombiana de Estadística 45, no. 1 (2022): 125–48. http://dx.doi.org/10.15446/rce.v45n1.93196.

Full text

Abstract:

We consider a finite population mixed model that accommodates response errors and show how to obtain optimal estimators of the finite population parameters in a pretest-posttest context. We illustrate the method with the estimation of the difference in gain between two interventions and consider a simulation study to compare the empirical version of the proposed estimator (obtained by replacing variance components with estimates) with the estimator obtained via covariance analysis usually employed in such settings. The results indicate that in many instances, the proposed estimator has a smaller mean squared error than that obtained from the standard analysis of covariance model.

APA, Harvard, Vancouver, ISO, and other styles

Smit, Merijn, and Konrad Kuijken. "Chasing the peak: optimal statistics for weak shear analyses." Astronomy & Astrophysics 609 (January 2018): A103. http://dx.doi.org/10.1051/0004-6361/201731410.

Full text

Abstract:

Context. Weak gravitational lensing analyses are fundamentally limited by the intrinsic distribution of galaxy shapes. It is well known that this distribution of galaxy ellipticity is non-Gaussian, and the traditional estimation methods, explicitly or implicitly assuming Gaussianity, are not necessarily optimal. Aims. We aim to explore alternative statistics for samples of ellipticity measurements. An optimal estimator needs to be asymptotically unbiased, efficient, and robust in retaining these properties for various possible sample distributions. We take the non-linear mapping of gravitational shear and the effect of noise into account. We then discuss how the distribution of individual galaxy shapes in the observed field of view can be modeled by fitting Fourier modes to the shear pattern directly. This allows scientific analyses using statistical information of the whole field of view, instead of locally sparse and poorly constrained estimates. Methods. We simulated samples of galaxy ellipticities, using both theoretical distributions and data for ellipticities and noise. We determined the possible bias Δe, the efficiency η and the robustness of the least absolute deviations, the biweight, and the convex hull peeling (CHP) estimators, compared to the canonical weighted mean. Using these statistics for regression, we have shown the applicability of direct Fourier mode fitting. Results. We find an improved performance of all estimators, when iteratively reducing the residuals after de-shearing the ellipticity samples by the estimated shear, which removes the asymmetry in the ellipticity distributions. We show that these estimators are then unbiased in the absence of noise, and decrease noise bias by more than ~30%. Our results show that the CHP estimator distribution is skewed, but still centered around the underlying shear, and its bias least affected by noise. We find the least absolute deviations estimator to be the most efficient estimator in almost all cases, except in the Gaussian case, where it’s still competitive (0.83 < η < 5.1) and therefore robust. These results hold when fitting Fourier modes, where amplitudes of variation in ellipticity are determined to the order of 10-3. Conclusions. The peak of the ellipticity distribution is a direct tracer of the underlying shear and unaffected by noise, and we have shown that estimators that are sensitive to a central cusp perform more efficiently, potentially reducing uncertainties by more than 50% and significantly decreasing noise bias. These results become increasingly important, as survey sizes increase and systematic issues in shape measurements decrease.

APA, Harvard, Vancouver, ISO, and other styles

Kou, Junke, Qinmei Huang, and Huijun Guo. "Pointwise Wavelet Estimations for a Regression Model in Local Hölder Space." Axioms 11, no. 9 (2022): 466. http://dx.doi.org/10.3390/axioms11090466.

Full text

Abstract:

This paper considers an unknown functional estimation problem in a regression model with multiplicative and additive noise. A linear wavelet estimator is first constructed by a wavelet projection operator. The convergence rate under the pointwise error of linear wavelet estimators is studied in local Hölder space. A nonlinear wavelet estimator is provided by the hard thresholding method in order to obtain an adaptive estimator. The convergence rate of the nonlinear estimator is the same as the linear estimator up to a logarithmic term. Finally, it should be pointed out that the convergence rates of two wavelet estimators are consistent with the optimal convergence rate on pointwise nonparametric estimation.

APA, Harvard, Vancouver, ISO, and other styles

Jin, Hanqing, and Shige Peng. "Optimal unbiased estimation for maximal distribution." Probability, Uncertainty and Quantitative Risk 6, no. 3 (2021): 189. http://dx.doi.org/10.3934/puqr.2021009.

Full text

Abstract:

Unbiased estimation for parameters of maximal distribution is a fundamental problem in the statistical theory of sublinear expectations. In this paper, we proved that the maximum estimator is the largest unbiased estimator for the upper mean and the minimum estimator is the smallest unbiased estimator for the lower mean.

APA, Harvard, Vancouver, ISO, and other styles

Zhang, Yan, Jiyuan Tao, Zhixiang Yin, and Guoqiang Wang. "Improved Large Covariance Matrix Estimation Based on Efficient Convex Combination and Its Application in Portfolio Optimization." Mathematics 10, no. 22 (2022): 4282. http://dx.doi.org/10.3390/math10224282.

Full text

Abstract:

The estimation of the covariance matrix is an important topic in the field of multivariate statistical analysis. In this paper, we propose a new estimator, which is a convex combination of the linear shrinkage estimation and the rotation-invariant estimator under the Frobenius norm. We first obtain the optimal parameters by using grid search and cross-validation, and then, we use these optimal parameters to demonstrate the effectiveness and robustness of the proposed estimation in the numerical simulations. Finally, in empirical research, we apply the covariance matrix estimation to the portfolio optimization. Compared to the existing estimators, we show that the proposed estimator has better performance and lower out-of-sample risk in portfolio optimization.

APA, Harvard, Vancouver, ISO, and other styles

Schladitz, Katja. "Surprising optimal estimators for the area fraction." Advances in Applied Probability 31, no. 4 (1999): 995–1001. http://dx.doi.org/10.1239/aap/1029955255.

Full text

Abstract:

For a random closed set X and a compact observation window W the mean coverage fraction of W can be estimated by measuring the area of W covered by X. Jensen and Gundersen, and Baddeley and Cruz-Orive described cases where a point counting estimator is more efficient than area measurement. We give two other examples, where at first glance unnatural estimators are not only better than the area measurement but by Grenander's Theorem have minimal variance. Whittle's Theorem is used to show that the point counting estimator in the original Jensen-Gundersen paradox is optimal for large randomly translated discs.

APA, Harvard, Vancouver, ISO, and other styles

Schladitz, Katja. "Surprising optimal estimators for the area fraction." Advances in Applied Probability 31, no. 04 (1999): 995–1001. http://dx.doi.org/10.1017/s0001867800009575.

Full text

Abstract:

APA, Harvard, Vancouver, ISO, and other styles

Neuhaus, Walther. "Optimal Estimation Under Linear Constraints." ASTIN Bulletin 26, no. 2 (1996): 233–45. http://dx.doi.org/10.2143/ast.26.2.563222.

Full text

Abstract:

AbstractThis paper shows how a multivariate Bayes estimator can be adjusted to satisfy a set of linear constraints. In the direct approach, the constraint is enforced by a restriction on the class of admissible estimators. In an alternative approach, the constraint is merely encouraged by a mixed risk function which penalises misbalance between the estimator and the constraint. The adjustment to the optimal unconstrained estimator is shown to depend on the risk function and the linear constraints only, not on the probability model underlying the Bayes estimator. Two practical examples are given, one of which involves reconciliation of independently assessed share values with current market values.

APA, Harvard, Vancouver, ISO, and other styles

Sun, Xiao Jun. "Globally Optimal Weighted Fusion White Noise Deconvolution Estimator." Advanced Materials Research 823 (October 2013): 422–27. http://dx.doi.org/10.4028/www.scientific.net/amr.823.422.

Full text

Abstract:

White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. A globally optimal weighted fusion white noise deconvolution estimator is presented for the multisensor linear discrete systems using the Kalman filtering method. It is derived from the centralized fusion white noise deconvolution estimator so that it is identical to the centralized fuser, i.e. it has the global optimality. Compared with the existing globally suboptimal distributed fusion white noise estimators, the proposed white noise fuser is given based on the local Kalman predictors, and the computation of complex covariance matrices is avoided. A simulation for the Bernoulli-Gaussian input white noise shows the effectiveness of the proposed results.

APA, Harvard, Vancouver, ISO, and other styles

Cao, Wenhan, Jianyu Chen, Jingliang Duan, et al. "Reinforced Optimal Estimator." IFAC-PapersOnLine 54, no. 20 (2021): 366–73. http://dx.doi.org/10.1016/j.ifacol.2021.11.201.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Chu, Tianpeng, Guoqing Qi, Yinya Li, and Andong Sheng. "Distributed Asynchronous Fusion Algorithm for Sensor Networks with Packet Losses." Discrete Dynamics in Nature and Society 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/957439.

Full text

Abstract:

This paper is concerned with the problem of distributed estimation fusion over peer-to-peer asynchronous sensor networks with random packet dropouts. A distributed asynchronous fusion algorithm is proposed via the covariance intersection method. First, local estimator is developed in an optimal batch fashion by constructing augmented measurement equations. Then the fusion estimator is designed to fuse local estimates in the neighborhood. Both local estimator and fusion estimator are developed by taking into account the random packet losses. The presented estimation method improves local estimates and reduces the estimate disagreement. Simulation results validate the effectiveness of the proposed distributed asynchronous fusion algorithm.

APA, Harvard, Vancouver, ISO, and other styles

Bassem Khalaf, Narjes, and Lekaa Ali Mohammed. "Comparison of Some Methods for Estimating Nonparametric Binary Logistic Regression." Journal of Economics and Administrative Sciences 29, no. 135 (2023): 56–67. http://dx.doi.org/10.33095/jeas.v29i135.2505.

Full text

Abstract:

In this research, the methods of Kernel estimator (nonparametric density estimator) were relied upon in estimating the two-response logistic regression, where the comparison was used between the method of Nadaraya-Watson and the method of Local Scoring algorithm, and optimal Smoothing parameter λ was estimated by the methods of Cross-validation and generalized Cross-validation, bandwidth optimal λ has a clear effect in the estimation process. It also has a key role in smoothing the curve as it approaches the real curve, and the goal of using the Kernel estimator is to modify the observations so that we can obtain estimators with characteristics close to the properties of real parameters, and based on medical data for patients with chronic lymphocytic leukemia and through the use of the Gaussian function and based on the comparison criterion (MSE) it was found that the Nadaraya -Watson method is the best because it obtained the lowest value for this criterion.

APA, Harvard, Vancouver, ISO, and other styles

Abrams, Aaron, Sandy Ganzell, Henry Landau, Zeph Landau, James Pommersheim, and Eric Zaslow. "Optimal Estimators for Threshold-Based Quality Measures." Journal of Probability and Statistics 2010 (2010): 1–15. http://dx.doi.org/10.1155/2010/752750.

Full text

Abstract:

We consider a problem in parametric estimation: givennsamples from an unknown distribution, we want to estimate which distribution, from a given one-parameter family, produced the data. Following Schulman and Vazirani (2005), we evaluate an estimator in terms of the chance of being within a specified tolerance of the correct answer, in the worst case. We provide optimal estimators for several families of distributions onℝ. We prove that for distributions on a compact space, there is always an optimal estimator that is translation invariant, and we conjecture that this conclusion also holds for any distribution onℝ. By contrast, we give an example showing that, it does not hold for a certain distribution on an infinite tree.

APA, Harvard, Vancouver, ISO, and other styles

Bii, Nelson Kiprono, Christopher Ouma Onyango, and John Odhiambo. "Boundary Bias Correction Using Weighting Method in Presence of Nonresponse in Two-Stage Cluster Sampling." Journal of Probability and Statistics 2019 (June 2, 2019): 1–8. http://dx.doi.org/10.1155/2019/6812795.

Full text

Abstract:

Kernel density estimators due to boundary effects are often not consistent when estimating a density near a finite endpoint of the support of the density to be estimated. To address this, researchers have proposed the application of an optimal bandwidth to balance the bias-variance trade-off in estimation of a finite population mean. This, however, does not eliminate the boundary bias. In this paper weighting method of compensating for nonresponse is proposed. Asymptotic properties of the proposed estimator of the population mean are derived. Under mild assumptions, the estimator is shown to be asymptotically consistent.

APA, Harvard, Vancouver, ISO, and other styles

Raphan, Martin, and Eero P. Simoncelli. "Least Squares Estimation Without Priors or Supervision." Neural Computation 23, no. 2 (2011): 374–420. http://dx.doi.org/10.1162/neco_a_00076.

Full text

Abstract:

Selection of an optimal estimator typically relies on either supervised training samples (pairs of measurements and their associated true values) or a prior probability model for the true values. Here, we consider the problem of obtaining a least squares estimator given a measurement process with known statistics (i.e., a likelihood function) and a set of unsupervised measurements, each arising from a corresponding true value drawn randomly from an unknown distribution. We develop a general expression for a nonparametric empirical Bayes least squares (NEBLS) estimator, which expresses the optimal least squares estimator in terms of the measurement density, with no explicit reference to the unknown (prior) density. We study the conditions under which such estimators exist and derive specific forms for a variety of different measurement processes. We further show that each of these NEBLS estimators may be used to express the mean squared estimation error as an expectation over the measurement density alone, thus generalizing Stein's unbiased risk estimator (SURE), which provides such an expression for the additive gaussian noise case. This error expression may then be optimized over noisy measurement samples, in the absence of supervised training data, yielding a generalized SURE-optimized parametric least squares (SURE2PLS) estimator. In the special case of a linear parameterization (i.e., a sum of nonlinear kernel functions), the objective function is quadratic, and we derive an incremental form for learning this estimator from data. We also show that combining the NEBLS form with its corresponding generalized SURE expression produces a generalization of the score-matching procedure for parametric density estimation. Finally, we have implemented several examples of such estimators, and we show that their performance is comparable to their optimal Bayesian or supervised regression counterparts for moderate to large amounts of data.

APA, Harvard, Vancouver, ISO, and other styles

Chen, Jia, and Junke Kou. "Nonparametric Pointwise Estimation for a Regression Model with Multiplicative Noise." Journal of Function Spaces 2021 (October 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/1599286.

Full text

Abstract:

In this paper, we consider a general nonparametric regression estimation model with the feature of having multiplicative noise. We propose a linear estimator and nonlinear estimator by wavelet method. The convergence rates of those regression estimators under pointwise error over Besov spaces are proved. It turns out that the obtained convergence rates are consistent with the optimal convergence rate of pointwise nonparametric functional estimation.

APA, Harvard, Vancouver, ISO, and other styles

Gong, Deren, Xiaowei Shao, Wei Li, and Dengping Duan. "Optimal linear attitude estimator and its recursive algorithm via geometric analysis." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 227, no. 1 (2012): 100–109. http://dx.doi.org/10.1177/0954410011428564.

Full text

Abstract:

A new optimal linear attitude estimator is proposed for single-point attitude estimation using geometric approach, and a recursive optimal linear attitude estimator is developed through filtering noisy measurements. Dot and cross products are taken in order to eliminate the unknown parameters of relationships between measurements and Gibbs vector. The optimality criterion, which does not coincide with Wahba’s constrained criterion, yields linear attitude estimate. A prior rotation is adopted to avoid singularity which occurs when the principal angle is close to π. The recursive algorithm is achieved for the purpose of improving attitude accuracy using all past measurements. For long-term space missions, memory fading concept is introduced into recursive optimal linear attitude estimator. The optimal relative weighting is obtained through minimizing error propagation, and an efficient modification is proposed to significantly reduce the sudden increase of attitude error of recursive optimal linear attitude estimator in special cases. Numerical simulations show that the estimate of optimal linear attitude estimator is almost identical with that of the famous QUaternion ESTimator, and the accuracy provided by recursive optimal linear attitude estimator is over an order magnitude higher than that of optimal linear attitude estimator or QUaternion ESTimator in most cases.

APA, Harvard, Vancouver, ISO, and other styles

OKHRIN, YAREMA, and WOLFGANG SCHMID. "ESTIMATION OF OPTIMAL PORTFOLIO WEIGHTS." International Journal of Theoretical and Applied Finance 11, no. 03 (2008): 249–76. http://dx.doi.org/10.1142/s0219024908004798.

Full text

Abstract:

The paper discusses finite sample properties of optimal portfolio weights, estimated expected portfolio return, and portfolio variance. The first estimator assumes the asset returns to be independent, while the second takes them to be predictable using a linear regression model. The third and the fourth approaches are based on a shrinkage technique and a Bayesian methodology, respectively. In the first two cases, we establish the moments of the weights and the portfolio returns. A consistent estimator of the shrinkage parameter for the third estimator is then derived. The advantages of the shrinkage approach are assessed in an empirical study.

APA, Harvard, Vancouver, ISO, and other styles

Zhang, Zhaoqi, Xuelei Feng, and Yong Shen. "Late Reverberant Spectral Variance Estimation for Single-Channel Dereverberation Using Adaptive Parameter Estimator." Applied Sciences 11, no. 17 (2021): 8054. http://dx.doi.org/10.3390/app11178054.

Full text

Abstract:

The estimation of the late reverberant spectral variance (LRSV) is of paramount importance in most reverberation suppression algorithms. This letter proposes an improved single-channel LRSV estimator based on Habets LRSV estimator by using an adaptive parameter estimator. Instead of estimating the direct-to-reverberation ratio (DRR), the proposed LRSV estimator directly estimates the parameter κ in a generalized statistical model since the experimental results show that even the κ calculated using measured ground truth DRR may not be the optimal parameter for the LRSV estimator. Experimental results using synthetic reverberant signals demonstrate the superiority of the proposed estimator to conventional approaches.

APA, Harvard, Vancouver, ISO, and other styles

Amano, Tomoyuki. "Asymptotic Optimality of Estimating Function Estimator for CHARN Model." Advances in Decision Sciences 2012 (July 3, 2012): 1–11. http://dx.doi.org/10.1155/2012/515494.

Full text

Abstract:

CHARN model is a famous and important model in the finance, which includes many financial time series models and can be assumed as the return processes of assets. One of the most fundamental estimators for financial time series models is the conditional least squares (CL) estimator. However, recently, it was shown that the optimal estimating function estimator (G estimator) is better than CL estimator for some time series models in the sense of efficiency. In this paper, we examine efficiencies of CL and G estimators for CHARN model and derive the condition that G estimator is asymptotically optimal.

APA, Harvard, Vancouver, ISO, and other styles

Díaz, Iván, and Mark J. van der Laan. "Targeted Data Adaptive Estimation of the Causal Dose–Response Curve." Journal of Causal Inference 1, no. 2 (2013): 171–92. http://dx.doi.org/10.1515/jci-2012-0005.

Full text

Abstract:

AbstractEstimation of the causal dose–response curve is an old problem in statistics. In a non-parametric model, if the treatment is continuous, the dose–response curve is not a pathwise differentiable parameter, and no -consistent estimator is available. However, the risk of a candidate algorithm for estimation of the dose–response curve is a pathwise differentiable parameter, whose consistent and efficient estimation is possible. In this work, we review the cross-validated augmented inverse probability of treatment weighted estimator (CV A-IPTW) of the risk and present a cross-validated targeted minimum loss–based estimator (CV-TMLE) counterpart. These estimators are proven consistent and efficient under certain consistency and regularity conditions on the initial estimators of the outcome and treatment mechanism. We also present a methodology that uses these estimated risks to select among a library of candidate algorithms. These selectors are proven optimal in the sense that they are asymptotically equivalent to the oracle selector under certain consistency conditions on the estimators of the treatment and outcome mechanisms. Because the CV-TMLE is a substitution estimator, it is more robust than the CV-AIPTW against empirical violations of the positivity assumption. This and other small sample size differences between the CV-TMLE and the CV-A-IPTW are explored in a simulation study.

APA, Harvard, Vancouver, ISO, and other styles

Pamososuryo, Atindriyo Kusumo, Fabio Spagnolo, and Sebastiaan Paul Mulders. "Analysis and calibration of optimal power balance rotor-effective wind speed estimation schemes for large-scale wind turbines." Wind Energy Science 10, no. 5 (2025): 987–1006. https://doi.org/10.5194/wes-10-987-2025.

Full text

Abstract:

Abstract. The size growth of modern wind turbines creates challenges in their control system design, particularly due to greater wind variability across larger rotor areas. As modern turbine control systems rely on the availability of accurate wind speed information, the increasing unrepresentativeness of pointwise measurement devices, such as anemometers, necessitates the incorporation of more representative rotor-effective wind speed (REWS) estimation. Classical REWS estimators, based on static power relations, often fail to account for dynamic changes, leading to inaccurate estimation. To overcome these challenges, this paper introduces a power-balance-based REWS estimation framework and splits the estimation problem into two modules: an aerodynamic power estimator and a wind speed estimate solver. Two possible aerodynamic power estimation techniques are discussed based on numerical derivative and state estimation. As state estimator calibration remained a challenge for varying wind turbine sizes, a gain-tailoring method for the performance calibration throughout a range of modern wind turbine sizes has been derived for the state-estimation-based aerodynamic power estimator. Two types of wind speed estimate solvers are analyzed, namely the continuous and iterative single-step methods. From the two modules, the best-performing methods – the state estimation aerodynamic power estimator and iterative single-step wind speed solver – are chosen to form the optimal power balance REWS estimator. The combined optimal estimator is validated through OpenFAST simulations of the National Renewable Energy Laboratory (NREL) 5 MW and IEA 22 MW turbines and compared against a baseline method. The proposed method demonstrates good tracking of the REWS, better noise resilience, and convenient estimator gain calibration across different turbine sizes.

APA, Harvard, Vancouver, ISO, and other styles

Choi, Won, Vladimir Shin, and Il Song. "Mean-Square Estimation of Nonlinear Functionals via Kalman Filtering." Symmetry 10, no. 11 (2018): 630. http://dx.doi.org/10.3390/sym10110630.

Full text

Abstract:

This paper focuses on estimation of a nonlinear functional of state vector (NFS) in discrete-time linear stochastic systems. The NFS represents a nonlinear multivariate functional of state variables, which can indicate useful information of a target system for control. The optimal mean-square estimator of a general NFS represents a function of the Kalman estimate and its error covariance. The polynomial functional of state vector is studied in detail. In this case an optimal estimation algorithm has a closed-form computational procedure. The novel mean-square quadratic estimator is derived. For a general NFS we propose to use the unscented transformation to calculate an optimal estimate. The obtained results are demonstrated on theoretical and practical examples with different types of NFS. Comparative analysis with suboptimal estimators for NFS is presented. The subsequent application of the proposed estimators to linear discrete-time systems demonstrates their practical effectiveness.

APA, Harvard, Vancouver, ISO, and other styles

RATHER, KHALID UL ISLAM, M. IQBAL JEELANI, M. YOUNIS SHAH, AFSHAN TABASSUM, and S. E. H. RIZVI. "DIFFERENCE-CUM-EXPONENTIAL EFFICIENT ESTIMATOR OF POPULATION VARIANCE." Journal of Science and Arts 22, no. 2 (2022): 367–74. http://dx.doi.org/10.46939/j.sci.arts-22.2-a10.

Full text

Abstract:

In the current investigation, we have suggested a Difference Cum-Exponential Type Efficient Estimator of Population variance of the study variable using information on the auxiliary variable. Up to the first order of approximation, the proposed estimator's bias and mean square error (MSE) expressions are derived and suggested optimum estimator is also found, with its optimal qualities are investigated. The suggested estimator is proven to be more competent than sample variance, classic ratio estimators based on Isaki , Singh et al. and Kadilar and Cingi estimators in [1-3]. Numerical study is also carried out by using real data sets.

APA, Harvard, Vancouver, ISO, and other styles

Zhou, Xiaoshuang, Xiulian Gao, Yukun Zhang, Xiuling Yin, and Yanfeng Shen. "Efficient Estimation for the Derivative of Nonparametric Function by Optimally Combining Quantile Information." Symmetry 13, no. 12 (2021): 2387. http://dx.doi.org/10.3390/sym13122387.

Full text

Abstract:

In this article, we focus on the efficient estimators of the derivative of the nonparametric function in the nonparametric quantile regression model. We develop two ways of combining quantile regression information to derive the estimators. One is the weighted composite quantile regression estimator based on the quantile weighted loss function; the other is the weighted quantile average estimator based on the weighted average of quantile regression estimators at a single quantile. Furthermore, by minimizing the asymptotic variance, the optimal weight vector is computed, and consequently, the optimal estimator is obtained. Furthermore, we conduct some simulations to evaluate the performance of our proposed estimators under different symmetric error distributions. Simulation studies further illustrate that both estimators work better than the local linear least square estimator for all the symmetric errors considered except the normal error, and the weighted quantile average estimator performs better than the weighted composite quantile regression estimator in most situations.

APA, Harvard, Vancouver, ISO, and other styles

Mortari, Daniele, F. Landis Markley, and Puneet Singla. "Optimal Linear Attitude Estimator." Journal of Guidance, Control, and Dynamics 30, no. 6 (2007): 1619–27. http://dx.doi.org/10.2514/1.29568.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Varathan, Nagarajah, and Pushpakanthie Wijekoon. "Optimal generalized logistic estimator." Communications in Statistics - Theory and Methods 47, no. 2 (2017): 463–74. http://dx.doi.org/10.1080/03610926.2017.1307406.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Mohammed Alomari, Huda. "Bayes Estimations for Parameter of the Poisson distribution with Progressive Schemes." Academic Journal of Applied Mathematical Sciences, no. 102 (October 9, 2024): 14–23. https://doi.org/10.32861/ajams.10.2.14.23.

Full text

Abstract:

This study introduces maximum likelihood and Bayesian approaches to Poisson parameter estimation using posterior distribution. I discuss three types of loss functions: the asymmetric linear exponential loss function, non-linear exponential loss function, and squared error loss function. Their performance is compared with the maximum likelihood estimator using mean squared error (MSE) as the test criterion. The proposed method with the classical estimator (maximum likelihood estimator) is better than that with the non-classical estimators for point estimation with different sample sizes. Maximum likelihood estimation provides the optimal performance in estimating the Poisson distribution, as evidenced by the asymptotically smallest MSE values. For small true parameter values the results reveal that the Bayesian approaches have good estimation performance.

APA, Harvard, Vancouver, ISO, and other styles

Horowitz, Joel L. "Optimal Rates of Convergence of Parameter Estimators in the Binary Response Model with Weak Distributional Assumptions." Econometric Theory 9, no. 1 (1993): 1–18. http://dx.doi.org/10.1017/s0266466600007301.

Full text

Abstract:

The smoothed maximum score estimator of the coefficient vector of a binary response model is consistent and, after centering and suitable normalization, asymptotically normally distributed under weak assumptions [5]. Its rate of convergence in probability is N−h/(2h+1), where h ≥ 2 is an integer whose value depends on the strength of certain smoothness assumptions. This rate of convergence is faster than that of the maximum score estimator of Manski [11,12], which converges at the rate N−1/3 under assumptions that are somewhat weaker than those of the smoothed estimator. In this paper I prove that under the assumptions of smoothed maximum score estimation, N−h/(2h+1) is the fastest achievable rate of convergence of an estimator of the coefficient vector of a binary response model. Thus, the smoothed maximum score estimator has the fastest possible rate of convergence. The rate of convergence is defined in a minimax sense so as to exclude superefficient estimators.

APA, Harvard, Vancouver, ISO, and other styles

Chadyšas, V. "Estimation of a Distribution function under Sampling on Two Occasions." Nonlinear Analysis: Modelling and Control 14, no. 3 (2009): 315–31. http://dx.doi.org/10.15388/na.2009.14.3.14498.

Full text

Abstract:

Estimation of the distribution function under sampling on two occasions with a simple random sampling design on each occasion is investigated. Composite regression and ratio type estimators are considered, using values of the study variable as auxiliary information obtained on the first occasion. The optimal estimator, in the sense of minimal variance, is also obtained. A simulation study, based on the real population data, is performed and the proposed estimators are compared by a simple estimator for a distribution function.

APA, Harvard, Vancouver, ISO, and other styles

Yu, Yuncai, Xinsheng Liu, Ling Liu, and Weisi Liu. "On adaptivity of wavelet thresholding estimators with negatively super-additive dependent noise." Mathematica Slovaca 69, no. 6 (2019): 1485–500. http://dx.doi.org/10.1515/ms-2017-0324.

Full text

Abstract:

Abstract This paper considers the nonparametric regression model with negatively super-additive dependent (NSD) noise and investigates the convergence rates of thresholding estimators. It is shown that the term-by-term thresholding estimator achieves nearly optimal and the block thresholding estimator attains optimal (or nearly optimal) convergence rates over Besov spaces. Additionally, some numerical simulations are implemented to substantiate the validity and adaptivity of the thresholding estimators with the presence of NSD noise.

APA, Harvard, Vancouver, ISO, and other styles

Kim, Tae-Kyoung, and Moonsik Min. "Reinforcement Learning-Aided Channel Estimator in Time-Varying MIMO Systems." Sensors 23, no. 12 (2023): 5689. http://dx.doi.org/10.3390/s23125689.

Full text

Abstract:

This paper proposes a reinforcement learning-aided channel estimator for time-varying multi-input multi-output systems. The basic concept of the proposed channel estimator is the selection of the detected data symbol in the data-aided channel estimation. To achieve the selection successfully, we first formulate an optimization problem to minimize the data-aided channel estimation error. However, in time-varying channels, the optimal solution is difficult to derive because of its computational complexity and the time-varying nature of the channel. To address these difficulties, we consider a sequential selection for the detected symbols and a refinement for the selected symbols. A Markov decision process is formulated for sequential selection, and a reinforcement learning algorithm that efficiently computes the optimal policy is proposed with state element refinement. Simulation results demonstrate that the proposed channel estimator outperforms conventional channel estimators by efficiently capturing the variation of the channels.

APA, Harvard, Vancouver, ISO, and other styles

Zerfaoui, Karima. "Strong uniform consistency of the mode estimator under α-mixing assumption and double truncation". STUDIES IN ENGINEERING AND EXACT SCIENCES 5, № 3 (2024): e12697. https://doi.org/10.54021/seesv5n3-060.

Full text

Abstract:

This article investigates the strong uniform consistency of mode estimators under the α-mixing hypothesis and double truncation. It addresses the challenges posed by dependent samples, particularly in the context of weak dependence, and provides theoretical results that establish the consistency of the mode estimator. The variable of interest, is truncated by two variables, (left) and (right), where observations are available only when . The model assumes independence between and , with a non-truncation probability . The nonparametric mode estimation relies on a kernel-based density estimator , where the kernel is a probability density function, and the bandwidth decreases as the sample size grows. Under regularity assumptions, theorems establish the uniform convergence of the density estimator and the mode estimator . The convergence rates are optimal and comparable to the complete and i.i.d. case. These results leverage specific conditions on the truncation structure and an optimal choice of the bandwidth . This study will be a valuable resource for scholars and practitioners interested in non-parametric kernel estimation methods for doubly truncated data.

APA, Harvard, Vancouver, ISO, and other styles

Bar-Shalom, Y., S. Challa, and H. A. P. Blom. "IMM estimator versus optimal estimator for hybrid systems." IEEE Transactions on Aerospace and Electronic Systems 41, no. 3 (2005): 986–91. http://dx.doi.org/10.1109/taes.2005.1541443.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Kim, B. K., and J. A. Bossi. "Wind shear estimation by frequency-shaped optimal estimator." Journal of Guidance, Control, and Dynamics 9, no. 2 (1986): 164–68. http://dx.doi.org/10.2514/3.20085.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Xiao, Min, Ting Chen, Kunpeng Huang, and Ruixing Ming. "Optimal Estimation for Power of Variance with Application to Gene-Set Testing." Journal of Systems Science and Information 8, no. 6 (2020): 549–64. http://dx.doi.org/10.21078/jssi-2020-549-16.

Full text

Abstract:

Abstract Detecting differential expression of genes in genom research (e.g., 2019-nCoV) is not uncommon, due to the cost only small sample is employed to estimate a large number of variances (or their inverse) of variables simultaneously. However, the commonly used approaches perform unreliable. Borrowing information across different variables or priori information of variables, shrinkage estimation approaches are proposed and some optimal shrinkage estimators are obtained in the sense of asymptotic. In this paper, we focus on the setting of small sample and a likelihood-unbiased estimator for power of variances is given under the assumption that the variances are chi-squared distribution. Simulation reports show that the likelihood-unbiased estimators for variances and their inverse perform very well. In addition, application comparison and real data analysis indicate that the proposed estimator also works well.

APA, Harvard, Vancouver, ISO, and other styles

Wei, Miaomiao, Yongsheng Zhu, Jun Sun, Xiangyang Lu, Xiaomin Mu, and Juncai Xu. "Performance Optimization in Frequency Estimation of Noisy Signals: Ds-IpDTFT Estimator." Sensors 23, no. 17 (2023): 7461. http://dx.doi.org/10.3390/s23177461.

Full text

Abstract:

This research presents a comprehensive study of the dichotomous search iterative parabolic discrete time Fourier transform (Ds-IpDTFT) estimator, a novel approach for fine frequency estimation in noisy exponential signals. The proposed estimator leverages a dichotomous search process before iterative interpolation estimation, which significantly reduces computational complexity while maintaining high estimation accuracy. An in-depth exploration of the relationship between the optimal parameter p and the unknown parameter δ forms the backbone of the methodology. Through extensive simulations and real-world experiments, the Ds-IpDTFT estimator exhibits superior performance relative to other established estimators, demonstrating robustness in noisy conditions and stability across varying frequencies. This efficient and accurate estimation method is a significant contribution to the field of signal processing and offers promising potential for practical applications.

APA, Harvard, Vancouver, ISO, and other styles

Zhu, Hong. "Design and Implementation of Vehicle Self-Navigation System in Urban Intelligent Traffic." Applied Mechanics and Materials 241-244 (December 2012): 2107–10. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2107.

Full text

Abstract:

Compared with the single sensor measuring, the complementary measuring with the dual sensor can solve some key problems in the vehicle self-navigation system. Two sensors’ data must be fused by the federal state estimator. Based on the Singer model, the system equation of Dead Reckoning sensor is nonlinear and the system equation of Global Positioning System sensor is linear. A two-level estimator is designed and implemented in such a way that two local estimators process the linear and nonlinear systems respectively, and the main estimator fuses the data from two local estimators, so that the optimal global state variables can be estimated. The simulation results show that the two-level estimator of dual sensor can increase the vehicle’s self-navigation precision and can be applied in urban intelligent traffic.

APA, Harvard, Vancouver, ISO, and other styles

Bernardelli, Michał, and Barbara Kowalczyk. "Optimal Allocation of the Sample in the Poisson Item Count Technique." Acta Universitatis Lodziensis. Folia Oeconomica 3, no. 335 (2018): 35–47. http://dx.doi.org/10.18778/0208-6018.335.03.

Full text

Abstract:

Indirect methods of questioning are of utmost importance when dealing with sensitive questions. This paper refers to the new indirect method introduced by Tian et al. (2014) and examines the optimal allocation of the sample to control and treatment groups. If determining the optimal allocation is based on the variance formula for the method of moments (difference in means) estimator of the sensitive proportion, the solution is quite straightforward and was given in Tian et al. (2014). However, maximum likelihood (ML) estimation is known from much better properties, therefore determining the optimal allocation based on ML estimators has more practical importance. This problem is nontrivial because in the Poisson item count technique the study sensitive variable is a latent one and is not directly observable. Thus ML estimation is carried out by using the expectation‑maximisation (EM) algorithm and therefore an explicit analytical formula for the variance of the ML estimator of the sensitive proportion is not obtained. To determine the optimal allocation of the sample based on ML estimation, comprehensive Monte Carlo simulations and the EM algorithm have been employed.

APA, Harvard, Vancouver, ISO, and other styles

Bassam, Ali M., and Mae L. Seto. "Derivation of a Low-Complexity-Pilot-Aided Doppler estimator for underwater acoustic applications." Journal of the Acoustical Society of America 157, no. 5 (2025): 3705–17. https://doi.org/10.1121/10.0036691.

Full text

Abstract:

A time-domain Doppler estimation method is developed using shift-orthogonal pilot sequences in underwater acoustic (UWA) orthogonal frequency division multiplexing systems. The derivation uses channel assumptions that cover a wide class of UWA communication environments. The proposed estimator is computationally lightweight, targeting applications with limited on-board processing power. Asymptotic analysis for high signal-to-noise ratios and a Cramer–Rao lower bound are presented. Although it is sub-optimal and is outperformed by some estimators, the proposed estimator outperforms many existing estimators found in the literature (in terms of mean square error).

APA, Harvard, Vancouver, ISO, and other styles

Gao, Yuan, and Zi Li Deng. "Covariance Intersection Fusion Kalman Estimators." Applied Mechanics and Materials 121-126 (October 2011): 750–54. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.750.

Full text

Abstract:

By the CI (Covariance Intersection) fusion algorithm, based on the ARMA innovation model, the two-sensor CI fusion Kalman estimators are presented for the systems with unknown cross-covariance. It is proved that their estimation accuracies are higher than those of the local Kalman estimators, and are lower than those of the optimal fused Kalman estimators. A Monte-Carlo simulation result shows that the actual accuracy of the presented CI fusion Kalman estimator are close to those of the optimal fused Kalman estimators with known cross-covariance.

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!