Log in

Relevant bibliographies by topics / Unbiased estimation of autocorrelation / Books

To see the other types of publications on this topic, follow the link: Unbiased estimation of autocorrelation.

Books on the topic 'Unbiased estimation of autocorrelation'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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

Select a source type:

Consult the top 15 books for your research on the topic 'Unbiased estimation of autocorrelation.'

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 books on a wide variety of disciplines and organise your bibliography correctly.

1

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

4

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

6

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

8

Optimal Unbiased Estimation of Variance Components. Springer, 2012.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

9

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

10

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

11

Geological Survey (U.S.), ed. KRIGING: An interactive program to determine the best linear unbiased estimation. [Reston, Va.?]: U.S. Dept. of the Interior, Geological Survey, 1985.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

12

Voinov, V. G., and M. S. Nikulin. Unbiased Estimators and their Applications: Volume 2: Multivariate Case (Mathematics and Its Applications). Springer, 1996.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

13

Mas, André, and Besnik Pumo. Linear Processes for Functional Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.3.

Full text

Abstract:

This article provides an overview of the basic theory and applications of linear processes for functional data, with particular emphasis on results published from 2000 to 2008. It first considers centered processes with values in a Hilbert space of functions before proposing some statistical models that mimic or adapt the scalar or finite-dimensional approaches for time series. It then discusses general linear processes, focusing on the invertibility and convergence of the estimated moments and a general method for proving asymptotic results for linear processes. It also describes autoregressive processes as well as two issues related to the general estimation problem, namely: identifiability and the inverse problem. Finally, it examines convergence results for the autocorrelation operator and the predictor, extensions for the autoregressive Hilbertian (ARH) model, and some numerical aspects of prediction when the data are curves observed at discrete points.

APA, Harvard, Vancouver, ISO, and other styles

14

McCleary, Richard, David McDowall, and Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

Full text

Abstract:

Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial autocorrelation function (PACF). Parameters of the tentative ARIMA noise model are estimated with maximum likelihood methods. If the estimates lie within the stationary-invertible bounds and are statistically significant, the residuals of the tentative model are diagnosed to determine whether the model’s residuals are not different than white noise. If the tentative model’s residuals satisfy this assumption, the statistically adequate model is accepted. Otherwise, the identification-estimation-diagnosis ARIMA noise model-building strategy continues iteratively until it yields a statistically adequate model. The Box-Jenkins ARIMA noise modeling strategy is illustrated with detailed analyses of twelve time series. The example analyses include non-Normal time series, stationary white noise, autoregressive and moving average time series, nonstationary time series, and seasonal time series. The time series models built in Chapter 3 are re-introduced in later chapters. Chapter 3 concludes with a discussion and demonstration of auxiliary modeling procedures that are not part of the Box-Jenkins strategy. These auxiliary procedures include the use of information criteria to compare models, unit root tests of stationarity, and co-integration.

APA, Harvard, Vancouver, ISO, and other styles

15

Karakoç, Ekrem. Cross-National Test of the Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198826927.003.0003.

Full text

Abstract:

The previous chapter posed the primary research question and offered a new theory that encompassed two interrelated arguments. This chapter produces three hypotheses derived from the new theory offered in Chapter 2. Chapter 3 tests these arguments in a large-N study using multivariate statistical analysis. The first section discusses the operationalization of our main dependent and independent variables. It will also briefly outline a set of control variables and what the literature predicts regarding their effect on spending and inequality. These factors range from economic factors (globalization, inflation, female labor participation, economic development), political factors (partisanship, electoral systems, election cycle), and demographic factors. To correct for problems associated with the nature of panel data models, such as endogeneity, heteroskedasticity, and autocorrelation, it uses the Arellano-Bond estimation, which uses the Generalized Method of Moments. The rest of the chapter presents the results and offers its interpretation and conclusion.

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!