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Journal articles on the topic 'Unbiased estimation of autocorrelation'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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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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3

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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7

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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9

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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10

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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11

Suesse, Thomas, and Ray Chambers. "Using Social Network Information for Survey Estimation." Journal of Official Statistics 34, no. 1 (March 1, 2018): 181–209. http://dx.doi.org/10.1515/jos-2018-0009.

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Abstract Model-based and model-assisted methods of survey estimation aim to improve the precision of estimators of the population total or mean relative to methods based on the nonparametric Horvitz-Thompson estimator. These methods often use a linear regression model defined in terms of auxiliary variables whose values are assumed known for all population units. Information on networks represents another form of auxiliary information that might increase the precision of these estimators, particularly if it is reasonable to assume that networked population units have similar values of the survey variable. Linear models that use networks as a source of auxiliary information include autocorrelation, disturbance, and contextual models. In this article we focus on social networks, and investigate how much of the population structure of the network needs to be known for estimation methods based on these models to be useful. In particular, we use simulation to compare the performance of the best linear unbiased predictor under a model that ignores the network with model-based estimators that incorporate network information. Our results show that incorporating network information via a contextual model seems to be the most appropriate approach. We also show that one does not need to know the full population network, but that knowledge of the partial network linking the sampled population units to the non-sampled population units is necessary. Finally, we also provide an estimator for the mean-squared error to make an informed decision about using the contextual information, as well as the results showing that this adaptive strategy leads to higher precision.

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Javed, Shazia, and Noor Atinah Ahmad. "Optimal preconditioned regularization of least mean squares algorithm for robust online learning1." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 3375–85. http://dx.doi.org/10.3233/jifs-191728.

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Despite its low computational cost, and steady state behavior, some well known drawbacks of the least means squares (LMS) algorithm are: slow rate of convergence and unstable behaviour for ill conditioned autocorrelation matrices of input signals. Several modified algorithms have been presented with better convergence speed, however most of these algorithms are expensive in terms of computational cost and time, and sometimes deviate from optimal Wiener solution that results in a biased solution of online estimation problem. In this paper, the inverse Cholesky factor of the input autocorrelation matrix is optimized to pre-whiten input signals and improve the robustness of the LMS algorithm. Furthermore, in order to have an unbiased solution, mean squares deviation (MSD) is minimized by improving convergence in misalignment. This is done by regularizing step-size adaptively in each iteration that helps in developing a highly efficient optimal preconditioned regularized LMS (OPRLMS) algorithm with adaptive step-size. Comparison of OPRLMS algorithm with other LMS based algorithms is given for unknown system identification and noise cancelation from ECG signal, that results in preference of the proposed algorithm over the other variants of LMS algorithm.

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13

Harini, Sri. "Estimation of Error Variance-Covariance Parameters Using Multivariate Geographically Weighted Regression Model." Abstract and Applied Analysis 2020 (February 1, 2020): 1–5. http://dx.doi.org/10.1155/2020/4657151.

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The Multivariate Geographically Weighted Regression (MGWR) model is a development of the Geographically Weighted Regression (GWR) model that takes into account spatial heterogeneity and autocorrelation error factors that are localized at each observation location. The MGWR model is assumed to be an error vector ε that distributed as a multivariate normally with zero vector mean and variance-covariance matrix Σ at each location ui,vi, which Σ is sized qxq for samples at the i-location. In this study, the estimated error variance-covariance parameters is obtained from the MGWR model using Maximum Likelihood Estimation (MLE) and Weighted Least Square (WLS) methods. The selection of the WLS method is based on the weighting function measured from the standard deviation of the distance vector between one observation location and another observation location. This test uses a statistical inference procedure by reducing the MGWR model equation so that the estimated error variance-covariance parameters meet the characteristics of unbiased. This study also provides researchers with an understanding of statistical inference procedures.

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14

Xu, Cheng-Dong, Jin-Feng Wang, Mao-Gui Hu, and Qing-Xiang Li. "Interpolation of Missing Temperature Data at Meteorological Stations Using P-BSHADE*." Journal of Climate 26, no. 19 (September 24, 2013): 7452–63. http://dx.doi.org/10.1175/jcli-d-12-00633.1.

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Abstract Some climate datasets are incomplete at certain places and times. A novel technique called the point estimation model of Biased Sentinel Hospitals-based Area Disease Estimation (P-BSHADE) is introduced to interpolate missing data in temperature datasets. Effectiveness of the technique was empirically evaluated in terms of an annual temperature dataset from 1950 to 2000 in China. The P-BSHADE technique uses a weighted summation of observed stations to derive unbiased and minimum error variance estimates of missing data. Both the ratio and covariance between stations were used in calculation of these weights. In this way, interpolation of missing data in the temperature dataset was improved, and best linear unbiased estimates (BLUE) were obtained. Using the same dataset, performance of P-BSHADE was compared against three estimators: kriging, inverse distance weighting (IDW), and spatial regression test (SRT). Kriging and IDW assume a homogeneous stochastic field, which may not be the case. SRT employs spatiotemporal data and has the potential to consider temperature nonhomogeneity caused by topographic differences, but has no objective function for the BLUE. Instead, P-BSHADE takes into account geographic spatial autocorrelation and nonhomogeneity, and maximizes an objective function for the BLUE of the target station. In addition to the theoretical advantages of P-BSHADE over the three other methods, case studies for an annual Chinese temperature dataset demonstrate its empirical superiority, except for the SRT from 1950 to 1970.

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15

Okui, Ryo. "Asymptotically unbiased estimation of autocovariances and autocorrelations for panel data with incidental trends." Economics Letters 112, no. 1 (July 2011): 49–52. http://dx.doi.org/10.1016/j.econlet.2011.03.013.

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Gu, Minghao, Shiliang Sun, and Yan Liu. "Dynamical Sampling with Langevin Normalization Flows." Entropy 21, no. 11 (November 10, 2019): 1096. http://dx.doi.org/10.3390/e21111096.

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In Bayesian machine learning, sampling methods provide the asymptotically unbiased estimation for the inference of the complex probability distributions, where Markov chain Monte Carlo (MCMC) is one of the most popular sampling methods. However, MCMC can lead to high autocorrelation of samples or poor performances in some complex distributions. In this paper, we introduce Langevin diffusions to normalization flows to construct a brand-new dynamical sampling method. We propose the modified Kullback-Leibler divergence as the loss function to train the sampler, which ensures that the samples generated from the proposed method can converge to the target distribution. Since the gradient function of the target distribution is used during the process of calculating the modified Kullback-Leibler, which makes the integral of the modified Kullback-Leibler intractable. We utilize the Monte Carlo estimator to approximate this integral. We also discuss the situation when the target distribution is unnormalized. We illustrate the properties and performances of the proposed method on varieties of complex distributions and real datasets. The experiments indicate that the proposed method not only takes the advantage of the flexibility of neural networks but also utilizes the property of rapid convergence to the target distribution of the dynamics system and demonstrate superior performances competing with dynamics based MCMC samplers.

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Peterson, Pearu, Mari Kalda, and Marko Vendelin. "Real-time determination of sarcomere length of a single cardiomyocyte during contraction." American Journal of Physiology-Cell Physiology 304, no. 6 (March 15, 2013): C519—C531. http://dx.doi.org/10.1152/ajpcell.00032.2012.

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Sarcomere length of a cardiomyocyte is an important control parameter for physiology studies on a single cell level; for instance, its accurate determination in real time is essential for performing single cardiomyocyte contraction experiments. The aim of this work is to develop an efficient and accurate method for estimating a mean sarcomere length of a contracting cardiomyocyte using microscopy images as an input. The novelty in developed method lies in 1) using unbiased measure of similarities to eliminate systematic errors from conventional autocorrelation function (ACF)-based methods when applied to region of interest of an image, 2) using a semianalytical, seminumerical approach for evaluating the similarity measure to take into account spatial dependence of neighboring image pixels, and 3) using a detrend algorithm to extract the sarcomere striation pattern content from the microscopy images. The developed sarcomere length estimation procedure has superior computational efficiency and estimation accuracy compared with the conventional ACF and spectral analysis-based methods using fast Fourier transform. As shown by analyzing synthetic images with the known periodicity, the estimates obtained by the developed method are more accurate at the subpixel level than ones obtained using ACF analysis. When applied in practice on rat cardiomyocytes, our method was found to be robust to the choice of the region of interest that may 1) include projections of carbon fibers and nucleus, 2) have uneven background, and 3) be slightly disoriented with respect to average direction of sarcomere striation pattern. The developed method is implemented in open-source software.

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18

Karydas, Christos, Miltiadis Iatrou, Dimitrios Kouretas, Anastasia Patouna, George Iatrou, Nikolaos Lazos, Sandra Gewehr, et al. "Prediction of Antioxidant Activity of Cherry Fruits from UAS Multispectral Imagery Using Machine Learning." Antioxidants 9, no. 2 (February 14, 2020): 156. http://dx.doi.org/10.3390/antiox9020156.

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In this research, a model for the estimation of antioxidant content in cherry fruits from multispectral imagery acquired from drones was developed, based on machine learning methods. For two consecutive cultivation years, the trees were sampled on different dates and then analysed for their fruits’ radical scavenging activity (DPPH) and Folin–Ciocalteu (FCR) reducing capacity. Multispectral images from unmanned aerial vehicles were acquired on the same dates with fruit sampling. Soil samples were collected throughout the study fields at the end of the season. Topographic, hydrographic and weather data also were included in modelling. First-year data were used for model-fitting, whereas second-year data for testing. Spatial autocorrelation tests indicated unbiased sampling and, moreover, allowed restriction of modelling input parameters to a smaller group. The optimum model employs 24 input variables resulting in a 6.74 root mean square error. Provided that soil profiles and other ancillary data are known in advance of the cultivation season, capturing drone images in critical growth phases, together with contemporary weather data, can support site- and time-specific harvesting. It could also support site-specific treatments (precision farming) for improving fruit quality in the long-term, with analogous marketing perspectives.

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19

Xu, Chengdong, Jinfeng Wang, and Qingxiang Li. "A New Method for Temperature Spatial Interpolation Based on Sparse Historical Stations." Journal of Climate 31, no. 5 (March 2018): 1757–70. http://dx.doi.org/10.1175/jcli-d-17-0150.1.

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Long-term grid historical temperature datasets are the foundation of climate change research. Datasets developed by traditional interpolation methods usually contain data for a period of less than 50 yr, with a relatively low spatial resolution owing to the sparse distribution of stations in the historical period. In this study, the point interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (P-BSHADE) method has been used to interpolate 1-km grids of monthly surface air temperatures in the historical period of 1900–50 in China. The method can be used to remedy the station bias resulting from sparse coverage, and it considers the characteristics of spatial autocorrelation and nonhomogeneity of the temperature distribution to obtain unbiased and minimum error variance estimates. The results have been compared with those from widely used methods such as kriging, inverse distance weighting (IDW), and a combined spline with kriging (TPS-KRG) method, both theoretically and empirically. The leave-one-out cross-validation method using a real dataset was implemented. The root-mean-square error (RMSE) [mean absolute error (MAE)] for P-BSHADE is 0.98°C (0.75°C), while those for TPS-KRG, kriging, and IDW are 1.46° (1.07°), 2.23° (1.51°), and 2.64°C (1.85°C), respectively. The results of validation using a simulated dataset also present the smallest error for P-BSHADE, demonstrating its empirical superiority. In addition to its empirical superiority, the method also can produce a map of the estimated error variance, representing the uncertainty of estimation.

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Kalantonis, Petros, Sotiria Schoina, Spyros Missiakoulis, and Constantin Zopounidis. "The Impact of the Disclosed R & D Expenditure on the Value Relevance of the Accounting Information: Evidence from Greek Listed Firms." Mathematics 8, no. 5 (May 6, 2020): 730. http://dx.doi.org/10.3390/math8050730.

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Although many empirical studies have focused on R & D performance models for markets globally, the available financial information for R & D expenditure is limited. In other words, can we assume that the reported accounting information for R & D investment is adequate and valuable? This study empirically investigates the effect of R & D reported information on the value relevance of the accounting information of firms’ financial statements. Specifically, using Ohlson’s equation, it is examined whether changes in stock prices are explained better when R & D factors are included in models, in conjunction with changes in book value and abnormal earnings. We focus on listed firms on the Athens Stock Exchange in order to explore whether R & D expenses are value relevant, in a market which has been affected for a long period by the global economic crisis of 2007. In our findings, we observe that the reported R & D expenses do not have any significant influence on the investors’ choices, in contrast to expectations based on the prior literature. Moreover, the panel data analysis employed in the paper overcomes common methodological problems (such as autocorrelation, multicollinearity, and heteroscedasticity) and allows the estimation of unbiased and efficient estimators.

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Panorama, Maya, Erdah Litriani, and Lilik Kurniasih. "PENGARUH INFRASTRUKTUR TERHADAP PERTUMBUHAN EKONOMI DI SUMATERA SELATAN TAHUN 2010-2014." I-ECONOMICS: A Research Journal on Islamic Economics 5, no. 1 (July 25, 2019): 90–101. http://dx.doi.org/10.19109/https://doi.org/10.19109/ieconomics.v5i1.3692.

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Infrastructure development in Indonesia has been going on for a long time and the investment has been very large. Infrastructure is an infrastructure. The availability of infrastructure is an important matter in the framework of economic developments in a region in accelerating the economic development process. Economic growth is an increase in the capacity of a long-term nation to produce various goods and services. The data used are panel data with a period from 2010 to 2014 for 17 regencies / cities in South Sumatra. To find results that are BLUE (Based Linear Unbiased Estimator), a panel test such as the Chow test and Hausman Test is conducted to complete the data with the characteristics as above. Than, tested classical assumptions such as normality, multicollinearity, heteroscedasticity, and autocorrelation. From the chow test and the best hausman test model in this study is the fixed effect model. While the results of the classic assumption test, f test t test, R2 test are from the four independent variables that have a significant effect on economic growth

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Artama, Evy Novia Nanda, Siti Maghfirotun Amin, and Tatag Yuli Eko Siswono. "Pengaruh Kecemasan Matematika Terhadap Hasil Belajar Matematika Siswa." JURNAL PENELITIAN PENDIDIKAN MATEMATIKA DAN SAINS 4, no. 1 (January 19, 2021): 34. http://dx.doi.org/10.26740/jppms.v4n1.p34-40.

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Abstrak — Penelitian ini memiliki tujuan mengetahui pengaruh kecemasan matematika terhadap hasil belajar matematika siswa. Penelitian dilaksanakan pada Kelas VIII SMPN 3 Sidoarjo Semester Genap Tahun Ajaran 2019/2020 dengan sampel penelitian pada kelas VIII-B dan kelas VIII-C dilakukan menggunakan teknik simple random sample. Penelitian ini termasuk dalam penelitian kuantitatif dengan menggunakan analisis regresi linier sederhana, kemudian dilakukan uji asumsi klasik. Untuk mengetahui pengaruh antar variabel dilakukan analisis regresi linier sederhana. Aplikasi SPSS versi 26 digunakan untuk membantu peneliti melakukan perhitungan. Untuk mengetahui model regresi baik atau tidak menurut BLUE (Best Linier Unbiased Estimator) dilakukan uji asumsi klasik. Instrumen yang digunakan berupa survey kecemasan matematika dan tes hasil belajar matematika. Dari hasil perhitungan mendapatkan persamaan sebagai berikut Y = 143,748−0,965X. Koefisien persamaan regresi memiliki nilai negatif artinya kecemasan matematika memiliki hubungan negatif terhadap hasil belajar matematika. Nilai koefisien korelasi sebesar -0,741 yang berarti mempunyai hubungan yang sangat rendah dan signifikan antara kecemasan matematika terhadap hasil belajar matematika. Koefisien determinasi sebesar 0,548 artinya kecemasan matematika terhadap hasil belajar matematika memiliki pengaruh sebesar 54,8%. Dari hasil uji asumsi klasik didapatkan bahwa hasil persamaan regresi linier, homoskedasitas, nonautokorelasi, nonmultikorelasi dan normalitas. Dari hasil analisis dapat disimpulkan bahwa kecemasan matematika memiliki pengaruh negatif yang signifikan terhadap hasil belajar matematika. Semakin tinggi kecemasan matematika akan berpengaruh terhadap rendahnya hasil belajar matematika siswa dan semakin rendah kecemasan matematika akan berpengaruh terhadap tingginya hasil belajar matematika siswa.Kata kunci: Kecemasan matematika, hasil belajar matematika Abstract — This study aims to determine the effect of mathematics anxiety on student mathematics learning outcomes. This research was conducted in Class VIII of SMPN 3 Sidoarjo Even Semester Academic Year 2019/2020 with research samples in class VIII-B and class VIII-C conducted with simple random sample technique. This research is included in quantitative research using simple linier regression analysis, then performed a classic assumption test. To determine the effect between variables, a simple linier regression analysis was performed. SPSS application version 26 is used to help researchers make conclusion. To determine whether the regression model is good or not according to BLUE (Best Linier Unbiased Estimator) classic assumption test is performed. The instrument used were mathematics anxiety survey and mathematics learning achievement test. From the calculation result get the following equation Y = 143,748−0,965X. The regression equation coefficient has a negative value meaning that mathematics anxiety has a negative relationship with the learning outcomes of mathematics. The correlation coefficient value is -0,741 which means it has a very low and significant relationship between mathematics anxiety and mathematics learning outcomes. The coefficient of determine of 0,548 means that mathematics learning outcomes has an effect of 54,8%. From the results of the classic assumption test it was found that the result of the linier regression equation, homoscedastic, non-autocorrelation, non-multicorrelation and normality. From the analysis it can be concluded that mathematics anxiety has a significant negative affect on mathematics anxiety will affect the low student mathematics learning outcomes and the lower mathematics anxiety will affect the higher student mathematics learning outcomes.Keywords: Mathematics anxiety, mathematics learning outcomes

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Barnes, Elizabeth A., and Randal J. Barnes. "Estimating Linear Trends: Simple Linear Regression versus Epoch Differences." Journal of Climate 28, no. 24 (December 15, 2015): 9969–76. http://dx.doi.org/10.1175/jcli-d-15-0032.1.

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Abstract Two common approaches for estimating a linear trend are 1) simple linear regression and 2) the epoch difference with possibly unequal epoch lengths. The epoch difference estimator for epochs of length M is defined as the difference between the average value over the last M time steps and the average value over the first M time steps divided by N − M, where N is the length of the time series. Both simple linear regression and the epoch difference are unbiased estimators for the trend; however, it is demonstrated that the variance of the linear regression estimator is always smaller than the variance of the epoch difference estimator for first-order autoregressive [AR(1)] time series with lag-1 autocorrelations less than about 0.85. It is further shown that under most circumstances if the epoch difference estimator is applied, the optimal epoch lengths are equal and approximately one-third the length of the time series. Additional results are given for the optimal epoch length at one end when the epoch length at the other end is constrained.

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Chang, Christopher C., and Dimitris N. Politis. "Robust Autocorrelation Estimation." Journal of Computational and Graphical Statistics 25, no. 1 (January 2, 2016): 144–66. http://dx.doi.org/10.1080/10618600.2014.969431.

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Gerow, Ken, and Charles E. McCulloch. "Simultaneously Model-Unbiased, Design-Unbiased Estimation." Biometrics 56, no. 3 (September 2000): 873–78. http://dx.doi.org/10.1111/j.0006-341x.2000.00873.x.

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NURLAILA, ZAKIAH, MADE SUSILAWATI, and DESAK PUTU EKA NILAKUSMAWATI. "PENERAPAN METODE NEWEY WEST DALAM MENGOREKSI STANDARD ERROR KETIKA TERJADI HETEROSKEDASTISITAS DAN AUTOKORELASI PADA ANALISIS REGRESI." E-Jurnal Matematika 6, no. 1 (January 20, 2017): 7. http://dx.doi.org/10.24843/mtk.2017.v06.i01.p142.

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Ordinary Least Squares (OLS) used to estimate the parameters in the regression analysis. If one of the assumptions is not fulfilled, the results of the OLS are no longer best, linear, and unbiased properties. The aim of this research was to find out the application of Newey West method to correct standard error when heteroscedasticity and autocorrelation occurred, and to compare the results of OLS with Newey West method on secondary and simulation data. OLS can still be used to estimate the regression parameter when heteroscedasticity and autocorrelation occurred. However, it will cause bias on standard error of parameter. A method which can correct the standard error of parameters to be unbiased parameter is Newey West method. The secondary data about Passenger Car Milage and data simulated contain heteroscedasticity and autocorrelation. The analysis showed that the Newey West method were known is able to correct standard error when heteroscedasticity and autocorrelation occurred on both of data. It was obtained that Newey west method with and changes the value of the bias standard error of OLS to be unbiased.

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Farebrother, R. W. "Unbiased L1and L∞estimation." Communications in Statistics - Theory and Methods 14, no. 8 (January 1985): 1941–62. http://dx.doi.org/10.1080/03610928508829022.

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Taner, M. Turhan, and Fulton Koehler. "Estimation of unbiased delays." GEOPHYSICS 63, no. 2 (March 1998): 738–42. http://dx.doi.org/10.1190/1.1444373.

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A large number of exploration processing procedures need the solution to the problem stated as, “Given a set of seismic traces, determine the common element.” This element could be the seismic wavelet, as in a set of common‐shot traces or as in a set of stack traces that need to be matched with synthetic seismograms. It could also be a pilot trace representing a noise‐free estimate of traces in a common‐depth‐point (CDP) gather to be used during an automatic time and phase statics computation. Most present computations use iteratively improved estimates of one sort or another. They assume that initial estimates are close enough to ensure that subsequent steps will produce more accurate results. The problem, of course, is the accuracy of the first unbiased estimation: when it fails, the rest becomes uncertain. In this paper we introduce a method that provides a robust solution to the problem of finding the common element. We also show that the method may be used to estimate both common wavelets and pilot traces conveniently with error correction facilities.

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Pitera, Marcin, and Thorsten Schmidt. "Unbiased estimation of risk." Journal of Banking & Finance 91 (June 2018): 133–45. http://dx.doi.org/10.1016/j.jbankfin.2018.04.016.

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Kim, Seong-in, and D. S. Bai. "On unbiased multinomial estimation." Statistics & Probability Letters 5, no. 1 (January 1987): 29–34. http://dx.doi.org/10.1016/0167-7152(87)90022-8.

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Kim, Seong-in, and D. S. Bai. "On unbiased multinomial estimation." Statistics & Probability Letters 5, no. 3 (April 1987): 225–30. http://dx.doi.org/10.1016/0167-7152(87)90045-9.

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32

Dürre, Alexander, Roland Fried, and Tobias Liboschik. "Robust estimation of (partial) autocorrelation." Wiley Interdisciplinary Reviews: Computational Statistics 7, no. 3 (March 31, 2015): 205–22. http://dx.doi.org/10.1002/wics.1351.

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33

Broersen, Piet M. T. "Historical Misconceptions in Autocorrelation Estimation." IEEE Transactions on Instrumentation and Measurement 56, no. 4 (August 2007): 1189–97. http://dx.doi.org/10.1109/tim.2007.900418.

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34

Reschenhofer, E. "Heteroscedasticity-robust estimation of autocorrelation." Communications in Statistics - Simulation and Computation 48, no. 4 (January 16, 2018): 1251–63. http://dx.doi.org/10.1080/03610918.2017.1408826.

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35

Lescornel, Hélène, Jean-Michel Loubes, and Claudie Chabriac. "Unbiased risk estimation method for covariance estimation." ESAIM: Probability and Statistics 18 (2014): 251–64. http://dx.doi.org/10.1051/ps/2013034.

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36

Ramaswamy, Murali, and George E. Ioup. "Autocorrelation estimation using constrained iterative spectral deconvolution." GEOPHYSICS 54, no. 3 (March 1989): 381–91. http://dx.doi.org/10.1190/1.1442663.

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Computing an autocorrelation conventionally produces a biased estimate, especially for a short data sequence. Windowing the autocorrelation can remove the bias but at the expense of violating the nonnegativity of the corresponding power spectrum. Constrained iterative deconvolution provides a basis for improving an autocorrelation estimate by reducing the bias while guaranteeing nonnegative definiteness. The length of the autocorrelation is increased in order to satisfy the nonnegativity constraints on the power spectral estimate. The constraints can also have significant effects on small, poorly determined values of the autocorrelation. The technique is applied to synthetic and real examples to show the improvements in the autocorrelation and power spectrum which are possible. The method is reasonably stable in the presence of noise and it approximately preserves the area of the power spectrum. Comparison to the maximum entropy technique shows that the iterative method gives power spectral resolution which is sometimes better and sometimes not as good, but that there are cases for which it is the more desirable approach.

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37

Van Dongen. "Unbiased estimation of individual asymmetry." Journal of Evolutionary Biology 13, no. 1 (January 2000): 107–12. http://dx.doi.org/10.1046/j.1420-9101.2000.00147.x.

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Agrawal, M. C., and A. B. Sthapit. "Unbiased ratio-type variance estimation." Statistics & Probability Letters 25, no. 4 (December 1995): 361–64. http://dx.doi.org/10.1016/0167-7152(94)00242-7.

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39

Pérez, R., C. Caso, and M. A. Gil. "Unbiased estimation of income inequality." Statistische Hefte 27, no. 1 (December 1986): 227–37. http://dx.doi.org/10.1007/bf02932569.

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Pan, J., R. Allemang, and H. Vold. "Unbiased estimation of operating shapes." Mechanical Systems and Signal Processing 6, no. 3 (May 1992): 275–85. http://dx.doi.org/10.1016/0888-3270(92)90030-m.

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41

Hu, Lei, Yuandong Xu, Fengshou Gu, Jing He, Niaoqing Hu, and Andrew Ball. "Autocorrelation Ensemble Average of Larger Amplitude Impact Transients for the Fault Diagnosis of Rolling Element Bearings." Energies 12, no. 24 (December 12, 2019): 4740. http://dx.doi.org/10.3390/en12244740.

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Rolling element bearings are one of the critical elements in rotating machinery of energy engineering systems. A defective roller of bearing moves in and out of the load zone during each revolution of the cage. Larger amplitude impact transients (LAITs) are produced when the defective roller passes the load zone centre and the defective area strikes the inner or outer races. A series of LAIT segments with higher signal to noise ratio are separated from a continuous vibration signal according to the bearing geometry and kinematics. In order to eliminate the phase errors between different LAIT segments that can arise from rotational speed fluctuations and roller slippages, unbiased autocorrelation is introduced to align the phases of LAIT segments. The unbiased autocorrelation signals make the ensemble averaging more accurate, and hence, archive enhanced diagnostic signatures, which are denoted as LAIT-AEAs for brevity. The diagnostic method based on LAIT separation and autocorrelation ensemble average (AEA) is evaluated with the datasets captured from real bearings of two different experiment benches. The validation results of the LAIT-AEAs are compared with the squared envelope spectrums (SESs) yielded based on two state-of-the-art techniques of Fast Kurtogram and Autogram.

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42

Chaudhuri, Arijit. "Unbiased Estimation of Total Rural Loans Advanced and Incurred in an Indian State Along with Unbiased Estimation of Variance in Estimation." Calcutta Statistical Association Bulletin 69, no. 1 (March 27, 2017): 71–75. http://dx.doi.org/10.1177/0008068317696549.

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Around the year 2000, the problem of reconciling the estimate of loans advanced by the banks and the estimate of loans incurred by the rural farmers was studied in the Indian Statistical Institute. Some approximately unbiased estimates were examined along with approximately unbiased estimates of their approximate variances. Utilizing “Constrained Network” sampling technique exactly unbiased counterparts are presented as alternatives.

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43

Nayak, Tapan K. "On best unbiased prediction and its relationships to unbiased estimation." Journal of Statistical Planning and Inference 84, no. 1-2 (March 2000): 171–89. http://dx.doi.org/10.1016/s0378-3758(99)00152-4.

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44

Sullivan, M. C. "Efficient autocorrelation estimation using relative magnitudes." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, no. 3 (March 1989): 445–47. http://dx.doi.org/10.1109/29.21717.

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Genossar, M. J., H. Lev-Ari, and T. Kailath. "Consistent estimation of the cyclic autocorrelation." IEEE Transactions on Signal Processing 42, no. 3 (March 1994): 595–603. http://dx.doi.org/10.1109/78.277851.

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de Cheveigné, Alain, and Hideki Kawahara. "Running autocorrelation method of F0 estimation." Journal of the Acoustical Society of America 109, no. 5 (May 2001): 2417. http://dx.doi.org/10.1121/1.4744551.

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47

Tao, Qiqing, Koichiro Tamura, Fabia U. Battistuzzi, and Sudhir Kumar. "A Machine Learning Method for Detecting Autocorrelation of Evolutionary Rates in Large Phylogenies." Molecular Biology and Evolution 36, no. 4 (January 23, 2019): 811–24. http://dx.doi.org/10.1093/molbev/msz014.

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Abstract New species arise from pre-existing species and inherit similar genomes and environments. This predicts greater similarity of the tempo of molecular evolution between direct ancestors and descendants, resulting in autocorrelation of evolutionary rates in the tree of life. Surprisingly, molecular sequence data have not confirmed this expectation, possibly because available methods lack the power to detect autocorrelated rates. Here, we present a machine learning method, CorrTest, to detect the presence of rate autocorrelation in large phylogenies. CorrTest is computationally efficient and performs better than the available state-of-the-art method. Application of CorrTest reveals extensive rate autocorrelation in DNA and amino acid sequence evolution of mammals, birds, insects, metazoans, plants, fungi, parasitic protozoans, and prokaryotes. Therefore, rate autocorrelation is a common phenomenon throughout the tree of life. These findings suggest concordance between molecular and nonmolecular evolutionary patterns, and they will foster unbiased and precise dating of the tree of life.

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Hirji, Karim F., Anastasios A. Tsiatis, and Cyrus R. Mehta. "Median Unbiased Estimation for Binary Data." American Statistician 43, no. 1 (February 1989): 7. http://dx.doi.org/10.2307/2685158.

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49

Wang, Jinliang. "Unbiased Relatedness Estimation in Structured Populations." Genetics 187, no. 3 (January 6, 2011): 887–901. http://dx.doi.org/10.1534/genetics.110.124438.

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Mathew, Thomas, and James D. Malley. "Optimal Unbiased Estimation of Variance Components." Journal of the American Statistical Association 84, no. 406 (June 1989): 622. http://dx.doi.org/10.2307/2289966.

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