Relevant bibliographies by topics / Unbiased estimation of autocorrelation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Unbiased estimation of autocorrelation'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Unbiased estimation of autocorrelation"

1

Okui, Ryo. "Asymptotically Unbiased Estimation of Autocovariances and Autocorrelations with Panel Data in the Presence of Individual and Time Effects." Journal of Time Series Econometrics 6, no. 2 (July 1, 2014): 129–81. http://dx.doi.org/10.1515/jtse-2013-0017.

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AbstractThis article proposes asymptotically unbiased estimators of autocovariances and autocorrelations for panel data with both individual and time effects. We show that the conventional autocovariance estimators suffer from the bias caused by the elimination of individual and time effects. The bias related to individual effects is proportional to the long-run variance, and it related to time effects is proportional to the value of the estimated autocovariance. For the conventional autocorrelation estimators, the elimination of time effects does not cause a bias while the elimination of individual effects does. We develop methods to estimate the long-run variance and propose bias-corrected estimators based on the proposed long-run variance estimator. We also consider the half-panel jackknife estimation for bias correction. The theoretical results are given by employing double asymptotics under which both the number of observations and the length of the time series tend to infinity. Monte Carlo simulations show that the asymptotic theory provides a good approximation to the actual bias and that the proposed bias-correction methods work well.
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2

Saputri, Ovi Delviyanti, Ferra Yanuar, and Dodi Devianto. "Simulation Study The Implementation of Quantile Bootstrap Method on Autocorrelated Error." CAUCHY 5, no. 3 (December 5, 2018): 95. http://dx.doi.org/10.18860/ca.v5i3.5349.

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<span lang="DE">Quantile regression is a regression method with the approach of separating or dividing data into certain quantiles by minimizing the number of absolute values from asymmetrical errors to overcome unfulfilled assumptions, including the presence of autocorrelation. The resulting model parameters are tested for accuracy using the bootstrap method. The bootstrap method is a parameter estimation method by re-sampling from the original sample as much as R replication. The bootstrap trust interval was then used as a test consistency test algorithm constructed on the estimator by the quantile regression method. And test the uncommon quantile regression method with bootstrap method. The data obtained in this test is data replication 10 times. The biasness is calculated from the difference between the quantile estimate and bootstrap estimation. Quantile estimation methods are said to be unbiased if the standard deviation bias is less than the standard bootstrap deviation. This study proves that the estimated value with quantile regression is within the bootstrap percentile confidence interval and proves that 10 times replication produces a better estimation value compared to other replication measures. Quantile regression method in this study is also able to produce unbiased parameter estimation values.</span>
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Zheng, Xiaogu. "Unbiased Estimation of Autocorrelations of Daily Meteorological Variables." Journal of Climate 9, no. 9 (September 1996): 2197–203. http://dx.doi.org/10.1175/1520-0442(1996)009<2197:ueoaod>2.0.co;2.

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Luskin, Robert C. "Wouldn't It Be Nice …? The Automatic Unbiasedness of OLS (and GLS)." Political Analysis 16, no. 3 (2008): 345–49. http://dx.doi.org/10.1093/pan/mpn003.

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In a recent issue of this journal, Larocca (2005) makes two notable claims about the best linear unbiasedness of ordinary least squares (OLS) estimation of the linear regression model. The first, drawn from McElroy (1967), is that OLS remains best linear unbiased in the face of a particular kind of autocorrelation (constant for all pairs of observations). The second, much larger and more heterodox, is that the disturbance need not be assumed uncorrelated with the regressors for OLS to be best linear unbiased. The assumption is unnecessary, Larocca says, because “orthogonality [of disturbance and regressors] is a property of all OLS estimates” (p. 192). Of course OLS's being best linear unbiased still requires that the disturbance be homoskedastic and (McElroy's loophole aside) nonautocorrelated, but Larocca also adds that the same automatic orthogonality obtains for generalized least squares (GLS), which is also therefore best linear unbiased, when the disturbance is heteroskedastic or autocorrelated.
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Buil-Gil, David, Angelo Moretti, Natalie Shlomo, and Juanjo Medina. "Applying the Spatial EBLUP to Place-Based Policing. Simulation Study and Application to Confidence in Police Work." Applied Spatial Analysis and Policy 13, no. 4 (March 9, 2020): 901–24. http://dx.doi.org/10.1007/s12061-020-09333-8.

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Abstract There is growing need for reliable survey-based small area estimates of crime and confidence in police work to design and evaluate place-based policing strategies. Crime and confidence in policing are geographically aggregated and police resources can be targeted to areas with the most problems. High levels of spatial autocorrelation in these variables allow for using spatial random effects to improve small area estimation models and estimates’ reliability. This article introduces the Spatial Empirical Best Linear Unbiased Predictor (SEBLUP), which borrows strength from neighboring areas, to place-based policing. It assesses the SEBLUP under different scenarios of number of areas and levels of spatial autocorrelation and provides an application to confidence in policing in London. The SEBLUP should be applied for place-based policing strategies when the variable’s spatial autocorrelation is medium/high, and the number of areas is large. Confidence in policing is higher in Central and West London and lower in Eastern neighborhoods.
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6

Xiong, Qing, Wei Hua Zhang, and Gui Ming Mei. "Quadratic Hilbert Transform Demodulation Based on Time-Delayed Correlation Treatment and EEMD." Advanced Materials Research 765-767 (September 2013): 2715–19. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2715.

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To deal with the demodulation problem of rolling bearing defect vibration signal in heavy noise, a new method based on time-delayed correlation algorithm and ensemble empirical mode decomposition (EEMD) is presented. Introduced the time-delayed autocorrelation de-noising principle. After the discretization and unbiased estimation of the original signals autocorrelation function , de-noising pretreatment is implemented by appending a rectangle window. Then an envelope signal can be obtained by the first Hilbert transform. After the EEMD decomposition, some interested intrinsic mode functions (IMFs) can be collected. By making the second Hilbert transform of the IMFs, we can get the local Hilbert marginal spectrum from which the defects in a rolling bearing can be identified. By repeated analysis of simulation signals and actual rolling bearings defect vibration signal, the results show that the proposed method is more effective than direct modulation or only time-delayed correlation demodulation or combine time-delayed correlation with EMD demodulation in de-noising and diagnosing the rolling bearing's defect information.
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Waldorp, Lourens. "Robust and Unbiased Variance of GLM Coefficients for Misspecified Autocorrelation and Hemodynamic Response Models in fMRI." International Journal of Biomedical Imaging 2009 (2009): 1–11. http://dx.doi.org/10.1155/2009/723912.

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As a consequence of misspecification of the hemodynamic response and noise variance models, tests on general linear model coefficients are not valid. Robust estimation of the variance of the general linear model (GLM) coefficients in fMRI time series is therefore essential. In this paper an alternative method to estimate the variance of the GLM coefficients accurately is suggested and compared to other methods. The alternative, referred to as the sandwich, is based primarily on the fact that the time series are obtained from multiple exchangeable stimulus presentations. The analytic results show that the sandwich is unbiased. Using this result, it is possible to obtain an exact statistic which keeps the 5% false positive rate. Extensive Monte Carlo simulations show that the sandwich is robust against misspeci cation of the autocorrelations and of the hemodynamic response model. The sandwich is seen to be in many circumstances robust, computationally efficient, and flexible with respect to correlation structures across the brain. In contrast, the smoothing approach can be robust to a certain extent but only with specific knowledge of the circumstances for the smoothing parameter.
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Torres, Sebastián M., and David A. Warde. "Staggered-PRT Sequences for Doppler Weather Radars. Part I: Spectral Analysis Using the Autocorrelation Spectral Density." Journal of Atmospheric and Oceanic Technology 34, no. 1 (January 2017): 51–63. http://dx.doi.org/10.1175/jtech-d-16-0071.1.

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AbstractThe autocorrelation spectral density (ASD) was introduced as a generalization of the classical periodogram-based power spectral density (PSD) and as an alternative tool for spectral analysis of uniformly sampled weather radar signals. In this paper, the ASD is applied to staggered pulse repetition time (PRT) sequences and is related to both the PSD and the ASD of the underlying uniform-PRT sequence. An unbiased autocorrelation estimator based on the ASD is introduced for use with staggered-PRT sequences when spectral processing is required. Finally, the strengths and limitations of the ASD for spectral analysis of staggered-PRT sequences are illustrated using simulated and real data.
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Okui, Ryo. "ASYMPTOTICALLY UNBIASED ESTIMATION OF AUTOCOVARIANCES AND AUTOCORRELATIONS WITH LONG PANEL DATA." Econometric Theory 26, no. 5 (February 17, 2010): 1263–304. http://dx.doi.org/10.1017/s0266466609990582.

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An important reason for analyzing panel data is to observe the dynamic nature of an economic variable separately from its time-invariant unobserved heterogeneity. This paper examines how to estimate the autocovariances of a variable separately from its time-invariant unobserved heterogeneity. When both cross-sectional and time series sample sizes tend to infinity, we show that the within-group autocovariances are consistent, although they are severely biased when the time series length is short. The biases have the leading term that converges to the long-run variance of the individual dynamics. This paper develops methods to estimate the long-run variance in panel data settings and to alleviate the biases of the within-group autocovariances based on the proposed long-run variance estimators. Monte Carlo simulations reveal that the procedures developed in this paper effectively reduce the biases of the estimators for small samples.
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Larocca, Roger. "Reconciling Conflicting Gauss-Markov Conditions in the Classical Linear Regression Model." Political Analysis 13, no. 2 (2005): 188–207. http://dx.doi.org/10.1093/pan/mpi011.

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This article reconciles conflicting accounts of Gauss-Markov conditions, which specify when ordinary least squares (OLS) estimators are also best linear unbiased (BLU) estimators. We show that exogeneity constraints that are commonly assumed in econometric treatments of the Gauss-Markov theorem are unnecessary for OLS estimates of the classical linear regression model to be BLU. We also generalize a set of necessary and sufficient conditions first established by McElroy (1967, Journal of the American Statistical Association 62:1302–1304), but not yet generally recognized in the econometric literature, that are appropriate for many political science applications. McElroy's conditions relax the traditional Gauss-Markov restriction on autocorrelation in the errors to allow a type of correlation, exchangeability, that has two desirable characteristics: (1) exchangeable data occur in a potentially important class of political science models, and (2) the form of autocorrelation that occurs in exchangeable data has a ready intuition. We thus show that a common class of sample selection models that does not satisfy the Gauss-Markov conditions specified in econometrics textbooks is, in fact, BLU under OLS estimation.
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Dissertations / Theses on the topic "Unbiased estimation of autocorrelation"

1

Kamanu, Timothy Kevin Kuria. "Location-based estimation of the autoregressive coefficient in ARX(1) models." Thesis, University of the Western Cape, 2006. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_9551_1186751947.

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In recent years, two estimators have been proposed to correct the bias exhibited by the leastsquares (LS) estimator of the lagged dependent variable (LDV) coefficient in dynamic regression models when the sample is finite. They have been termed as &lsquo
mean-unbiased&rsquo
and &lsquo
medianunbiased&rsquo
estimators. Relative to other similar procedures in the literature, the two locationbased estimators have the advantage that they offer an exact and uniform methodology for LS estimation of the LDV coefficient in a first order autoregressive model with or without exogenous regressors i.e. ARX(1).


However, no attempt has been made to accurately establish and/or compare the statistical properties among these estimators, or relative to those of the LS estimator when the LDV coefficient is restricted to realistic values. Neither has there been an attempt to 
compare their performance in terms of their mean squared error (MSE) when various forms of the exogenous regressors are considered. Furthermore, only implicit confidence intervals have been given for the &lsquo
medianunbiased&rsquo
estimator. Explicit confidence bounds that are directly usable for inference are not available for either estimator. In this study a new estimator of the LDV coefficient is proposed
the &lsquo
most-probably-unbiased&rsquo
estimator. Its performance properties vis-a-vis the existing estimators are determined and compared when the parameter space of the LDV coefficient is restricted. In addition, the following new results are established: (1) an explicit computable form for the density of the LS estimator is derived for the first time and an efficient method for its numerical evaluation is proposed
(2) the exact bias, mean, median and mode of the distribution of the LS estimator are determined in three specifications of the ARX(1) model
(3) the exact variance and MSE of LS estimator is determined
(4) the standard error associated with the determination of same quantities when simulation rather than numerical integration method is used are established and the methods are compared in terms of computational time and effort
(5) an exact method of evaluating the density of the three estimators is described
(6) their exact bias, mean, variance and MSE are determined and analysed
and finally, (7) a method of obtaining the explicit exact confidence intervals from the distribution functions of the estimators is proposed.


The discussion and results show that the estimators are still biased in the usual sense: &lsquo
in expectation&rsquo
. However the bias is substantially reduced compared to that of the LS estimator. The findings are important in the specification of time-series regression models, point and interval estimation, decision theory, and simulation.

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2

Zhang, Keshu. "Best linear unbiased estimation fusion with constraints." ScholarWorks@UNO, 2003. http://louisdl.louislibraries.org/u?/NOD,86.

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Thesis (Ph. D.)--University of New Orleans, 2003.
Title from electronic submission form. "A dissertation ... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Electrical Engineering"--Dissertation t.p. Vita. Includes bibliographical references.
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Cipperly, George Edward. "Direct scene parameter estimation from autocorrelation data." Diss., The University of Arizona, 1992. http://hdl.handle.net/10150/186058.

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Several aspects of extracting scene object information directly from the associated autocorrelation (or spectrum modulus) data arrays are investigated. Emphasis is on the particular scenario in which the scene can be modelled by a small set of dispersed objects with associated position, size, shape, and brightness parameters. These parameters may completely define a scene, they may contain the information of interest in a more complex scene, or they may merely constitute a reasonable first approximation to an arbitrary scene. A typical two step approach to estimating such parameters is to first use phase retrieval/image reconstruction techniques to estimate an associated image, and then apply pattern recognition techniques to extract the important information from it. The work described here focusses on eliminating the image recovery step and estimating parameter values directly from the autocorrelation. This task naturally separates into several distinct sub-problems, the first of which is extracting the significant features from the autocorrelation. This is a pattern recognition problem with special consideration to the unique features of autocorrelations. From these feature positions, the number of objects in the scene and their relative positions are next deduced, and finally, the individual object sizes, shapes and brightnesses are extracted. Optional further analysis is described in which the object parameter estimates are further refined by seeking a Maximum Likelihood estimate with regard to the data array. Alternatively, the initial estimates could be used to generate a trial image for an iterative phase retrieval procedure to reconstruct the full scene. Since the trial estimate already contains the major features of the scene, convergence to the correct solution should be both faster and better assured. The phase retrieval problem has been well studied and is not investigated here. For each of these sub-problems, the logical or mathematical development of the solution is presented, implementing computer algorithms are described, and theoretical and practical limitations are discussed.
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Chen, Donghui 1970. "Median-unbiased estimation in linear autoregressive time series models." Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/9044.

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Li, Huilin. "Small area estimation an empirical best linear unbiased prediction approach /." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7600.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.
Thesis research directed by: Mathematical Statistics Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Sall, Cheikh Ahmed Tidiane. "Dynamique et persistance de l’inflation dans l’UEMOA : le rôle des facteurs globaux, régionaux et nationaux." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM1085/document.

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La thèse étudie la dynamique et la persistance de l’inflation dans les pays en développement, particulièrement ceux des pays de la Zone UEMOA, en mettant en exergue les spécificités de ces économies. Le premier chapitre, consacré à l’évaluation de la persistance, révèle que le degré de persistance de l'inflation est faible dans ces pays, ce qui constitue un atout pour les autorités monétaires. Dans le chapitre 2, il a été défini un cadre théorique plus approprié à l’analyse de la persistance de l’inflation dans les pays de la sous-région. L’approche a permis de montrer que le degré de persistance de l’inflation dans ces pays ne dépendait pas uniquement des politiques monétaire et de change, mais aussi négativement du poids du secteur vivrier local dans l’économie. Dans le chapitre 3, la thèse analyse les écarts d’inflation dans les pays membres de l’UEMOA, en examinant la β-convergence des différentiels d'inflation. Les estimations révèlent que, d’une part, les écarts d’inflation se sont fortement réduits à l’intérieur de l'Union et que, d’autre part, ils restent fortement persistants avec la zone Euro. Le chapitre 4 est consacré à l’évaluation du rôle des différents facteurs et utilise ensuite une spécification spatiale en panel, pour tester les effets de contagion entre pays. Les estimations indiquent une prédominance des facteurs globaux et des effets de contagion entre pays dont l'ampleur dépend du poids des exportations de chaque pays vers les autres pays de la sous région
This thesis examines the inflation dynamics and persistence in developing countries, especially in the UEMOA zone, highlighting the specificities of these economies. The first chapter, reveals that the inflation persistence degree, in these countries, is low which represents an asset to the monetary authorities. In Chapter 2, it was defined a more appropriate theoretical framework to analyze the inflation persistence in the countries of the sub-region. The approach allowed to demonstrate that the inflation persistence degree in these countries is not only dependent on monetary and exchange rate policies, but also negatively to the weight of local food sector in the economy. Chapter 3, analyzes the inflation differentials in the UEMOA member countries, by examining the β - convergence of inflation differentials. Estimations show that the inflation differentials are greatly reduced within the Union and they are highly persistent with the Euro zone. Chapter 4, is devoted to assessing the role of various factors and then uses a spatial panel specification to test the spillover effect between countries. Estimations indicate a predominance of global factors and contagion between countries whose magnitude depends on the weight of exports to other countries in the sub-region
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Kalender, Emre. "Parametric Estimation Of Clutter Autocorrelation Matrix For Ground Moving Target Indication." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615313/index.pdf.

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In airborne radar systems with Ground Moving Target Indication (GMTI) mode, it is desired to detect the presence of targets in the interference consisting of noise, ground clutter, and jamming signals. These interference components usually mask the target return signal, such that the detection requires suppression of the interference signals. Space-time adaptive processing is a widely used interference suppression technique which uses temporal and spatial information to eliminate the effects of clutter and jamming and enables the detection of moving targets with small radial velocity. However, adaptive estimation of the interference requires high computation capacity as well as large secondary sample data support. The available secondary range cells may be fewer than required due to non-homogeneity problems and computational capacity of the radar system may not be sufficient for the computations required. In order to reduce the computational load and the required number of secondary data for estimation, parametric methods use a priori information on the structure of the clutter covariance matrix. Space Time Auto-regressive (STAR) filtering, which is a parametric adaptive method, and full parametric model-based approaches for interference suppression are proposed as alternatives to STAP in the literature. In this work, space time auto-regressive filtering and model-based GMTI approaches are investigated. Performance of these approaches are evaluated by both simulated and flight test data and compared with the performance of sample matrix inversion space time adaptive processing.
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Hu, Qilin. "Autocorrelation-based factor analysis and nonlinear shrinkage estimation of large integrated covariance matrix." Thesis, London School of Economics and Political Science (University of London), 2016. http://etheses.lse.ac.uk/3551/.

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The first part of my thesis deals with the factor modeling for high-dimensional time series based on a dimension-reduction viewpoint. we allow the dimension of time series N to be as large as, or even larger than the sample size of the time series. The estimation of the factor loading matrix and subsequently the factors are done via an eigenanalysis on a non-negative definite matrix constructed from autocorrelation matrix. The method is dubbed as AFA. We give explicit comparison of the convergence rates between AFA with PCA. We show that AFA possesses the advantage over PCA when dealing with small dimension time series for both one step and two step estimations, while at large dimension, the performance is still comparable. The second part of my thesis considers large integrated covariance matrix estimation. While the use of intra-day price data increases the sample size substantially for asset allocation, the usual realized covariance matrix still suffers from bias contributed from the extreme eigenvalues when the number of assets is large. We introduce a novel nonlinear shrinkage estimator for the integrated volatility matrix which shrinks the extreme eigenvalues of a realized covariance matrix back to acceptable level, and enjoys a certain asymptotic efficiency at the same time, all at a high dimensional setting where the number of assets can have the same order as the number of data points. Compared to a time-variation adjusted realized covariance estimator and the usual realized covariance matrix, our estimator demonstrates favorable performance in both simulations and a real data analysis in portfolio allocation. This include a novel maximum exposure bound and an actual risk bound when our estimator is used in constructing the minimum variance portfolio.
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9

Zhao, Zhanlue. "Performance Appraisal of Estimation Algorithms and Application of Estimation Algorithms to Target Tracking." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/394.

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This dissertation consists of two parts. The first part deals with the performance appraisal of estimation algorithms. The second part focuses on the application of estimation algorithms to target tracking. Performance appraisal is crucial for understanding, developing and comparing various estimation algorithms. In particular, with the evolvement of estimation theory and the increase of problem complexity, performance appraisal is getting more and more challenging for engineers to make comprehensive conclusions. However, the existing theoretical results are inadequate for practical reference. The first part of this dissertation is dedicated to performance measures which include local performance measures, global performance measures and model distortion measure. The second part focuses on application of the recursive best linear unbiased estimation (BLUE) or lineae minimum mean square error (LMMSE) estimation to nonlinear measurement problem in target tracking. Kalman filter has been the dominant basis for dynamic state filtering for several decades. Beyond Kalman filter, a more fundamental basis for the recursive best linear unbiased filtering has been thoroughly investigated in a series of papers by Dr. X. Rong Li. Based on the so-called quasirecursive best linear unbiased filtering technique, the constraints of the Kalman filter Linear-Gaussian assumptions can be relaxed such that a general linear filtering technique for nonlinear systems can be achieved. An approximate optimal BLUE filter is implemented for nonlinear measurements in target tracking which outperforms the existing method significantly in terms of accuracy, credibility and robustness.
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10

Miladinovic, Branko. "Kernel density estimation of reliability with applications to extreme value distribution." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002760.

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Books on the topic "Unbiased estimation of autocorrelation"

1

Optimal unbiased estimation of variance components. Berlin: Springer-Verlag, 1986.

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Malley, James D. Optimal Unbiased Estimation of Variance Components. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2.

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Estrella, Arturo. Consistent covariance matrix estimation in probit models with autocorrelated errors. [New York, N.Y.]: Federal Reserve Bank of New York, 1998.

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4

Cashin, Paul. An unbiased appraisal of purchasing power parity. [Washington, D.C.]: International Monetary Fund, Research Department, 2001.

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5

Rahiala, Markku. On the identification and estimation of multiple input transfer function models with autocorrelated errors. Helsinki: Research Institute of the Finnish Economy, 1985.

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S, Nikulin M., ed. Unbiased estimators and their applications. Dordrecht: Kluwer Academic Publishers, 1993.

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Choi, Chi-Young. Unbiased estimation of the half-life to ppp convergence in panel data. Cambridge, MA: National Bureau of Economic Research, 2004.

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Optimal Unbiased Estimation of Variance Components. Springer, 2012.

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9

Malley, J. D. Optimal unbiased estimation of variance components. Springer, 1986.

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Amina Ali Abd El-Fattah Saleh. Nonlinear unbiased estimators that dominate the intra-block estimator. 1986.

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Book chapters on the topic "Unbiased estimation of autocorrelation"

1

Dixit, Ulhas Jayram. "Unbiased Estimation." In Examples in Parametric Inference with R, 39–107. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0889-4_2.

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Keener, Robert W. "Unbiased Estimation." In Theoretical Statistics, 61–83. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-93839-4_4.

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Rose, Colin, and Murray D. Smith. "Unbiased Parameter Estimation." In Springer Texts in Statistics, 325–47. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4612-2072-5_10.

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Kiefer, Jack Carl. "Linear Unbiased Estimation." In Introduction to Statistical Inference, 81–136. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4613-9578-2_5.

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Sinha, Bimal K., and Bikas K. Sinha. "Unbiased sequential binomial estimation." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 75–85. Hayward, CA: Institute of Mathematical Statistics, 1992. http://dx.doi.org/10.1214/lnms/1215458839.

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Akahira, Masafumi, and Kei Takeuchi. "General Discussions on Unbiased Estimation." In Non-Regular Statistical Estimation, 1–19. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2554-6_1.

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Bickel, P. J., and E. L. Lehmann. "Unbiased Estimation in Convex Families." In Selected Works of E. L. Lehmann, 301–13. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4614-1412-4_26.

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Ghosh, J. K. "A Note on Unbiased Estimation." In Statistical Information and Likelihood, 329–32. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3894-2_20.

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Nguyen, Hung T., and Gerald S. Rogers. "Unbiased Estimation: The Vector Case." In Springer Texts in Statistics, 113–18. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-8914-9_18.

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Voinov, V. G., and M. S. Nikulin. "Applications of Unbiased Estimation Theory." In Unbiased Estimators and Their Applications, 247–304. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1970-2_3.

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Conference papers on the topic "Unbiased estimation of autocorrelation"

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Ai, Qingyao, Keping Bi, Cheng Luo, Jiafeng Guo, and W. Bruce Croft. "Unbiased Learning to Rank with Unbiased Propensity Estimation." In SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3209978.3209986.

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Villarrubia, J. S., and B. D. Bunday. "Unbiased estimation of linewidth roughness." In Microlithography 2005, edited by Richard M. Silver. SPIE, 2005. http://dx.doi.org/10.1117/12.599981.

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Piet M. T. Broersen. "Persistent Misconceptions in Autocorrelation Estimation." In 2006 IEEE Instrumentation and Measurement Technology. IEEE, 2006. http://dx.doi.org/10.1109/imtc.2006.236536.

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Broersen, Piet M. T. "Persistent Misconceptions in Autocorrelation Estimation." In 2006 IEEE Instrumentation and Measurement Technology. IEEE, 2006. http://dx.doi.org/10.1109/imtc.2006.328236.

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Phoon, K. K. "Bootstrap Estimation of Sample Autocorrelation Functions." In GeoCongress 2006. Reston, VA: American Society of Civil Engineers, 2006. http://dx.doi.org/10.1061/40803(187)107.

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Wang, Zhaoyang, Baihai Zhang, Senchun Chai, Lingguo Cui, and Yuting Bai. "Unbiased estimation localization for wireless sensor networks." In 2018 Chinese Control And Decision Conference (CCDC). IEEE, 2018. http://dx.doi.org/10.1109/ccdc.2018.8408276.

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Gonzalez, Gustavo, Fernando Gregorio, and Juan Cousseau. "Low complexity block-based unbiased frequency estimation." In 2011 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2011. http://dx.doi.org/10.1109/iscas.2011.5937754.

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Deledalle, Charles-Alban, Samuel Vaiter, Gabriel Peyre, Jalal Fadili, and Charles Dossal. "Unbiased risk estimation for sparse analysis regularization." In 2012 19th IEEE International Conference on Image Processing (ICIP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icip.2012.6467544.

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Nagarajappa*, Nirupama, and Peter Cary. "Unbiased surface-consistent scalar estimation by crosscorrelation." In SEG Technical Program Expanded Abstracts 2015. Society of Exploration Geophysicists, 2015. http://dx.doi.org/10.1190/segam2015-5909720.1.

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Adamson, R. B. A., and A. M. Steinberg. "Experimental Quantum State Estimation in Mutually Unbiased Bases." In International Conference on Quantum Information. Washington, D.C.: OSA, 2008. http://dx.doi.org/10.1364/icqi.2008.qwb2.

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Reports on the topic "Unbiased estimation of autocorrelation"

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Peters, Keith, and Steven Kay. Unbiased Estimation of the Phase of a Sinusoid. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada525814.

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March-Leuba, Jose A. Autocorrelation Function Statistics and Implication to Decay Ratio Estimation. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1234357.

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Hero, A. O. A Cramer-Rao Type Lower Bound for Essentially Unbiased Parameter Estimation. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada246666.

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Choi, Chi-Young, Nelson Mark, and Donggyu Sul. Unbiased Estimation of the Half-Life to PPP Convergence in Panel Data. Cambridge, MA: National Bureau of Economic Research, July 2004. http://dx.doi.org/10.3386/w10614.

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Stock, James, and Mark Watson. Asymptotically Median Unbiased Estimation of Coefficient Variance in a Time Varying Parameter Model. Cambridge, MA: National Bureau of Economic Research, August 1996. http://dx.doi.org/10.3386/t0201.

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