Relevant bibliographies by topics / Linear prediction

Academic literature on the topic 'Linear prediction'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Linear prediction"

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Matsumoto, T. "Online Bayesian Modeling and Prediction of Nonlinear Systems." Methods of Information in Medicine 46, no. 02 (2007): 96–101. http://dx.doi.org/10.1055/s-0038-1625379.

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Summary Objectives : Given time-series data from an unknown target system, one often wants to build a model for the system behind the data and make predictions. If the target system can be assumed to be linear, there are means of modeling and predicting the target system in question. If, however, one cannot assume the system is linear, various linear theories have natural limitations in terms of modeling and predictive capabilities. This paper attempts to construct a model from time-series data and make an online prediction when the linear assumption is not valid. Methods : The problem is formulated within a Bayesian framework implemented by the Sequential Monte Carlo method. Online Bayesian learning/prediction requires computation of a posterior distribution in a sequential manner as each datum arrives. The Sequential Monte Carlo method computes the importance weight in order to draw sample from the posterior distribution. The scheme is tested against time-series data from a noisy Rossler system. Results : The test time-series data is the x-coordinate of the trajectory generated by a noisy Roessler system. Attempts are made with regard to online reconstruction of the attractor and online prediction of the time-series data. Conclusions : The proposed algorithm appears to be functional. The algorithm should be tested against real world data.
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Rather, Akhter Mohiuddin. "A Hybrid Intelligent Method of Predicting Stock Returns." Advances in Artificial Neural Systems 2014 (September 7, 2014): 1–7. http://dx.doi.org/10.1155/2014/246487.

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This paper proposes a novel method for predicting stock returns by means of a hybrid intelligent model. Initially predictions are obtained by a linear model, and thereby prediction errors are collected and fed into a recurrent neural network which is actually an autoregressive moving reference neural network. Recurrent neural network results in minimized prediction errors because of nonlinear processing and also because of its configuration. These prediction errors are used to obtain final predictions by summation method as well as by multiplication method. The proposed model is thus hybrid of both a linear and a nonlinear model. The model has been tested on stock data obtained from National Stock Exchange of India. The results indicate that the proposed model can be a promising approach in predicting future stock movements.
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Bai, Chao, and Haiqi Li. "Simultaneous prediction in the generalized linear model." Open Mathematics 16, no. 1 (August 24, 2018): 1037–47. http://dx.doi.org/10.1515/math-2018-0087.

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AbstractThis paper studies the prediction based on a composite target function that allows to simultaneously predict the actual and the mean values of the unobserved regressand in the generalized linear model. The best linear unbiased prediction (BLUP) of the target function is derived. Studies show that our BLUP has better properties than some other predictions. Simulations confirm its better finite sample performance.
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den Brinker, Albertus C., Harish Krishnamoorthi, and Evgeny A. Verbitskiy. "Similarities and Differences Between Warped Linear Prediction and Laguerre Linear Prediction." IEEE Transactions on Audio, Speech, and Language Processing 19, no. 1 (January 2011): 24–33. http://dx.doi.org/10.1109/tasl.2010.2042130.

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Sharifi, Alireza, Yagob Dinpashoh, and Rasoul Mirabbasi. "Daily runoff prediction using the linear and non-linear models." Water Science and Technology 76, no. 4 (April 28, 2017): 793–805. http://dx.doi.org/10.2166/wst.2017.234.

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Runoff prediction, as a nonlinear and complex process, is essential for designing canals, water management and planning, flood control and predicting soil erosion. There are a number of techniques for runoff prediction based on the hydro-meteorological and geomorphological variables. In recent years, several soft computing techniques have been developed to predict runoff. There are some challenging issues in runoff modeling including the selection of appropriate inputs and determination of the optimum length of training and testing data sets. In this study, the gamma test (GT), forward selection and factor analysis were used to determine the best input combination. In addition, GT was applied to determine the optimum length of training and testing data sets. Results showed the input combination based on the GT method with five variables has better performance than other combinations. For modeling, among four techniques: artificial neural networks, local linear regression, an adaptive neural-based fuzzy inference system and support vector machine (SVM), results indicated the performance of the SVM model is better than other techniques for runoff prediction in the Amameh watershed.
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Möst, Lisa, Matthias Schmid, Florian Faschingbauer, and Torsten Hothorn. "Predicting birth weight with conditionally linear transformation models." Statistical Methods in Medical Research 25, no. 6 (September 30, 2016): 2781–810. http://dx.doi.org/10.1177/0962280214532745.

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Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs.
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Li, Tongxin, Ruixiao Yang, Guannan Qu, Guanya Shi, Chenkai Yu, Adam Wierman, and Steven Low. "Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions." ACM SIGMETRICS Performance Evaluation Review 50, no. 1 (June 20, 2022): 107–8. http://dx.doi.org/10.1145/3547353.3522658.

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We study the problem of learning-augmented predictive linear quadratic control. Our goal is to design a controller that balances "consistency'', which measures the competitive ratio when predictions are accurate, and "robustness'', which bounds the competitive ratio when predictions are inaccurate. We propose a novel λ-confident controller and prove that it maintains a competitive ratio upper bound of 1 + min {O(λ2ε)+ O(1-λ)2,O(1)+O(λ2)} where λ∈ [0,1] is a trust parameter set based on the confidence in the predictions, and ε is the prediction error. Further, motivated by online learning methods, we design a self-tuning policy that adaptively learns the trust parameter λ with a competitive ratio that depends on ε and the variation of system perturbations and predictions. We show that its competitive ratio is bounded from above by 1+O(ε) /(Θ)(1)+Θ(ε))+O(μVar) where μVar measures the variation of perturbations and predictions. It implies that by automatically adjusting the trust parameter online, the self-tuning scheme ensures a competitive ratio that does not scale up with the prediction error ε.
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Sreehari, E., and Pradeep G. S. Ghantasala. "Climate Changes Prediction Using Simple Linear Regression." Journal of Computational and Theoretical Nanoscience 16, no. 2 (February 1, 2019): 655–58. http://dx.doi.org/10.1166/jctn.2019.7785.

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The rise in global temperatures, frequent natural disasters and rising sea levels, reducing Polar Regions have made the problem of understanding and predicting these global climate phenomena. Prediction is a matter of prime importance and they are run as computer simulations to predict climate variables such as temperature, precipitation, rainfall and etc. The agricultural country called India in which 60% of the people depending upon the agriculture. Rain fall prediction is the most important task for predicting early prediction of rainfall May helps to peasant's as well as for the people because most of the people in India can be depends upon the agriculture. The paper represents simple linear regression technique for the early prediction of rainfall. It can helps to farmers for taking appropriate decisions on crop yielding. As usually at the same time there may be a scope to analyze the occurrence of floods or droughts. The simple linear regression analysis methodology applied on the dataset collected over six years of Coonor in Nilagris district from Tamil Nadu state. The experiment and our simple linear regression methodology exploit the appropriate results for the rain fall.
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Gardner, William R. "Burst excited linear prediction." Journal of the Acoustical Society of America 102, no. 6 (1997): 3250. http://dx.doi.org/10.1121/1.419565.

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Jimenez, Daniel A. "Piecewise Linear Branch Prediction." ACM SIGARCH Computer Architecture News 33, no. 2 (May 2005): 382–93. http://dx.doi.org/10.1145/1080695.1070002.

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Dissertations / Theses on the topic "Linear prediction"

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Stevens, D. A. "Non-linear prediction for speech processing." Thesis, Swansea University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.639110.

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For over 20 years linear prediction has been one of the most widely used methods for analysing speech signals. Linear predictors have been used to model the vocal tract in all areas of speech processing from speech recognition to speech synthesis. However, Teager showed as early as 1980 by measuring the flow within the vocal tract during the pronunciation of a vowel sound, that the vocal tract is a non-linear system. As such the standard linear predictors are unable to model all the vocal tract information available in the speech signal. This work looks at replacing or complementing the standard linear models with non-linear ones in order to improve the modelling of the vocal tract. Several different methods of both generating and implementing non-linear models of the vocal tract are assessed to see how much improvement in prediction can be achieved by using non-linear models, either in place of, or complementing, the standard linear models. Two basic approaches to non-linear prediction have been used. The first of these is to configure a multi-layered perceptron (MLP) as a non-linear predictor and then to train the MLP to predict the speech signal. The second method is known as a split function approach as it effectively splits the overall predictor function into smaller sub-functions each of which requires a less complex predictor function than the whole. This second method uses a classification stage to determine what type of speech is present and then uses a separate predictor for each of the classifications. Initial results using a single MLP predictor proved ineffective, returning gains of 0.1 to 0.3 dB in excess of the standard LPC. This is thought to be due to an inability of the networks used to model the full dynamic complexity of the speech signal. However with the split function predictors it is shown that relatively high prediction gains can be achieved using a few simple sub-functions. With four linear sub-functions gains of 2.1 dB have been achieved over the standard LPC.
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Pottorff, Robert Thomas. "Video Prediction with Invertible Linear Embeddings." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7577.

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Using recently popularized invertible neural network We predict future video frames from complex dynamic scenes. Our invertible linear embedding (ILE) demonstrates successful learning, prediction and latent state inference. In contrast to other approaches, ILE does not use any explicit reconstruction loss or simplistic pixel-space assumptions. Instead, it leverages invertibility to optimize the likelihood of image sequences exactly, albeit indirectly.Experiments and comparisons against state of the art methods over synthetic and natural image sequences demonstrate the robustness of our approach, and a discussion of future work explores the opportunities our method might provide to other fields in which the accurate analysis and forecasting of non-linear dynamic systems is essential.
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Kalantzis, Eugenia. "Prediction of soil corrosivity using linear polarization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq29604.pdf.

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Albanis, George T. "Financial prediction using non linear classification techniques." Thesis, City University London, 2001. http://openaccess.city.ac.uk/8289/.

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In this thesis, we explore the ability of statistical classification methods to predict financial events in the bond and stock markets. Our classification methods include conventional Linear Dicriminant Analysis (LDA), and a number of less familiar non-linear techniques such as Probabilistic Neural Network (PNN), Learning Vector Quanization (LVQ), Oblique Classifer (OCI), and Ripper Rule Induction (RRI).
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Zhang, Lei. "Code excited linear prediction with multi-pulse codebooks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq24279.pdf.

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Islam, Tamanna. "Interpolation of linear prediction coefficients for speech coding." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0034/MQ64229.pdf.

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Hawkes, Richard Nathanael. "Linear state models for volatility estimation and prediction." Thesis, Brunel University, 2007. http://bura.brunel.ac.uk/handle/2438/7138.

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This thesis concerns the calibration and estimation of linear state models for forecasting stock return volatility. In the first two chapters I present aspects of financial modelling theory and practice that are of particular relevance to the theme of this present work. In addition to this I review the literature concerning these aspects with a particular emphasis on the area of dynamic volatility models. These chapters set the scene and lay the foundations for subsequent empirical work and are a contribution in themselves. The structure of the models employed in the application chapters 4,5 and 6 is the state-space structure, or alternatively the models are known as unobserved components models. In the literature these models have been applied in the estimation of volatility, both for high frequency and low frequency data. As opposed to what has been carried out in the literature I propose the use of these models with Gaussian components. I suggest the implementation of these for high frequency data for short and medium term forecasting. I then demonstrate the calibration of these models and compare medium term forecasting performance for different forecasting methods and model variations as well as that of GARCH and constant volatility models. I then introduce implied volatility measurements leading to two-state models and verify whether this derivative-based information improves forecasting performance. In chapter 6I compare different unobserved components models' specification and forecasting performance. The appendices contain the extensive workings of the parameter estimates' standard error calculations.
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Freij, G. J. "Enhanced sequential adaptive linear prediction for speech encoding." Thesis, University of Liverpool, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356268.

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Eatwell, Karen Anne. "Remediation of instability in Best Linear Unbiased Prediction." Thesis, University of Pretoria, 2013. http://hdl.handle.net/2263/40245.

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In most breeding programmes breeders use phenotypic data obtained in breeding trials to rank the performance of the parents or progeny on pre-selected performance criteria. Through this ranking the best candidates are identified and selected for breeding or production purposes. Best Linear Unbiased Prediction (BLUP), is an efficient selection method to use, combining information into a single index. Unbalanced or messy data is frequently found in tree breeding trial data. Trial individuals are related and a degree of correlation is expected between individuals over sites, which can lead to collinearity in the data which may lead to instability in certain selection models. A high degree of collinearity may cause problems and adversely affect the prediction of the breeding values in a BLUP selection index. Simulation studies have highlighted that instability is a concern and needs to be investigated in experimental data. The occurrence of instability, relating to collinearity, in BLUP of tree breeding data and possible methods to deal with it were investigated in this study. Case study data from 39 forestry breeding trials (three generations) of Eucalyptus grandis and 20 trials of Pinus patula (two generations) were used. A series of BLUP predictions (rankings) using three selection traits and 10 economic weighting sets were made. Backward and forward prediction models with three different matrix inversion techniques (singular value decomposition, Gaussian elimination - partial and full pivoting) and an adapted ridge regression technique were used in calculating BLUP indices. A Delphi and Clipper version of the same BLUP programme which run with different computational numerical precision were used and compared. Predicted breeding values (forward prediction) were determined in the F1 and F2 E. grandis trials and F1 P. patula trials and realised breeding performance (backward prediction) was determined in the F2 and F3 E. grandis trials and F2 P. patula trials. The accuracy (correlation between the predicted breeding values and realised breeding performance) was estimated in order to assess the efficiency of the predictions and evaluate the different matrix inversion methods. The magnitude of the accuracy (correlations) was found to mostly be of acceptable magnitude when compared to the heritability of the compound weighted trait in the F1F2 E. grandis scenarios. Realised genetic gains were also calculated for each method used. Instability was observed in both E. grandis and P. patula breeding data in the study, and this may cause a significant loss in realised genetic gains. Instability can be identified by examining the matrix calculated from the product of the phenotypic covariance matrix with its inverse, for deviations from the expected identity pattern. Results of this study indicate that it may not always be optimal to use a higher numerical precision programme when there is collinearity in the data and instability in the matrix calculations. In some cases, where there is a large amount of collinearity, the use of a higher precision programme for BLUP calculations can significantly increase or decrease the accuracy of the rankings. The different matrix inversion techniques particularly SVD and adapted ridge regression did not perform much better than the full pivoting technique. The study found that it is beneficial to use the full pivoting Gaussian elimination matrix inversion technique in preference to the partial pivoting Gaussian elimination matrix inversion technique for both high and lower numerical precision programmes.
Thesis (PhD)--University of Pretoria, 2013.
gm2014
Genetics
unrestricted
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Mahmood, Arshad. "Rainfall prediction in Australia : Clusterwise linear regression approach." Thesis, Federation University Australia, 2017. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/159251.

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Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbours methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was improved by adding the input variables in most locations. To assess the influence of geographic regions on the performance of prediction models and to compare the prediction performance of models, we trained models with the best combination of input variables and predicted monthly and weekly rainfall over the test periods. The results of this analysis confirm that the prediction performance of all selected models varied considerably with geographic regions for both weekly and monthly rainfall predictions. It is found that models have the lowest prediction error in the desert climate zone and highest in subtropical and tropical zones. The results also demonstrate that the proposed algorithm is capable of finding the patterns and trends of the observations for monthly and weekly rainfall predictions in all geographic regions. In desert, tropical and subtropical climate zones, the proposed method outperform other methods in most locations for both monthly and weekly rainfall predictions. In temperate and grassland zones the prediction performance of the proposed model is better in some locations while in the remaining locations it is slightly lower than the other models.
Doctor of Philosophy
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Books on the topic "Linear prediction"

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Strobach, Peter. Linear Prediction Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75206-3.

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Vaidyanathan, P. P. The Theory of Linear Prediction. Cham: Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-02527-3.

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Baram, Yoram. Linear prediction of stationary vector sequences. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1988.

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Cain, Michael. Normal prediction under linear-quadratic loss. Aberystwyth: University of Wales, Aberystwyth, Dept. of Economics, 1993.

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Foundations of the prediction process. Oxford: Clarendon Press, 1992.

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International, C. A. B., ed. Linear models for the prediction of animal breeding values. Wallingford: CAB International, 1996.

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Linear prediction theory: A mathematical basis for adaptive systems. Berlin: Springer-Verlag, 1990.

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Linear models for the prediction of animal breeding values. 2nd ed. Cambridge, MA: CABI Pub., 2005.

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Mrode, R., ed. Linear models for the prediction of animal breeding values. Wallingford: CABI, 2014. http://dx.doi.org/10.1079/9781780643915.0000.

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Mrode, R. A., and R. Thompson, eds. Linear models for the prediction of animal breeding values. Wallingford: CABI, 2005. http://dx.doi.org/10.1079/9780851990002.0000.

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Book chapters on the topic "Linear prediction"

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You, Yuli. "Linear Prediction." In Audio Coding, 53–72. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1754-6_4.

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Gersho, Allen, and Robert M. Gray. "Linear Prediction." In Vector Quantization and Signal Compression, 83–129. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3626-0_4.

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Benesty, Jacob, Jingdong Chen, and Yiteng Huang. "Linear Prediction." In Springer Handbook of Speech Processing, 121–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-49127-9_7.

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Balakrishnan, N., and Rita Aggarwala. "Linear Prediction." In Progressive Censoring, 139–65. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1334-5_8.

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Stein, Michael L. "Linear Prediction." In Springer Series in Statistics, 1–14. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1494-6_1.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Linear Mixed Models." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 141–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_5.

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AbstractThe linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework. We illustrate the use of linear mixed models by using the predictor several components such as environments, genotypes, and genotype × environment interaction. Also, the linear mixed model is illustrated under a multi-trait framework that is important in the prediction performance when the degree of correlation between traits is moderate or large. We illustrate the use of single-trait and multi-trait linear mixed models and provide the R codes for performing the analyses.
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Faraway, Julian J. "Prediction." In Linear Models with Python, 53–60. 10th ed. First edition. | Boca Raton : CRC Press, 2021. | Series: Chapman & Hall/CRC texts in statistical science: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781351053419-4.

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Schmidli, Heinz. "Linear Multivariate Prediction." In Contributions to Statistics, 16–37. Heidelberg: Physica-Verlag HD, 1995. http://dx.doi.org/10.1007/978-3-642-50015-2_3.

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Dutoit, Thierry. "Linear Prediction Synthesis." In Text, Speech and Language Technology, 201–28. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5730-8_8.

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Vaseghi, Saeed V. "Linear Prediction Models." In Advanced Signal Processing and Digital Noise Reduction, 185–213. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-92773-6_7.

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Conference papers on the topic "Linear prediction"

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Giacobello, Daniele, Manohar N. Murthi, Mads Graesboll Christensen, Soren Holdt Jensen, and Marc Moonen. "Re-estimation of linear predictive parameters in sparse linear prediction." In 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers. IEEE, 2009. http://dx.doi.org/10.1109/acssc.2009.5470202.

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Chen, Xueqin, Yibiao Yu, and Heming Zhao. "F0 prediction from linear predictive cepstral coefficient." In 2014 Sixth International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2014. http://dx.doi.org/10.1109/wcsp.2014.6992061.

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Venkitaraman, Aran, Saikat Chatterjee, and Peter Handel. "Graph linear prediction results in smaller error than standard linear prediction." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362377.

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Pohjalainen, Jouni, and Paavo Alku. "Gaussian mixture linear prediction." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854813.

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Chen, Wenliang, Zhenghua Li, and Min Zhang. "Linear and Non-Linear Models for Purchase Prediction." In RecSys '15: Ninth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2813448.2813518.

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Fucile, Fabio, Gabriele Bulian, and Claudio Lugni. "Prediction Error Statistics in Deterministic Linear Ship Motion Forecasting." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77456.

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Deterministic ship motions predictions methodologies represent a promising emerging approach, which could be embedded in decision support systems for certain types of operation. The typically envisioned prediction chain starts from the remote sensing of the wave elevation through wave radar technology. An estimated wave field is then fitted to the data, it is propagated in space and time, and it is finally fed to a ship motion prediction model. Prediction time horizons, typically, are practically limited to the order of minutes. Deterministic predictions are, however, inevitably associated with prediction uncertainty which is seldom quantified. This paper, therefore, presents a semi-analytical methodology for the estimation of ship motion prediction error statistics in ensemble domain as function of the forecasting time, assuming linear Gaussian irregular waves and stationary linear ship motions. This information can be used, for instance, to supplement deterministic forecasting with corresponding confidence intervals. The paper describes the theoretical background of the developed methodology and reports some numerical application examples.
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Jian Zhang, D. Smith, and Zhuo Chen. "Linear finite state Markov chain predictor for channel prediction." In 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC 2012). IEEE, 2012. http://dx.doi.org/10.1109/pimrc.2012.6362698.

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Pereira, Fernando C. N. "Linear models for structure prediction." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-2.

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Hönig, Florian, Georg Stemmer, Christian Hacker, and Fabio Brugnara. "Revising Perceptual Linear Prediction (PLP)." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-138.

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Waterschoot, Toon van, and Marc Moonen. "Linear prediction of audio signals." In Interspeech 2007. ISCA: ISCA, 2007. http://dx.doi.org/10.21437/interspeech.2007-239.

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Reports on the topic "Linear prediction"

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Koffman, Andrew D. High-dimensional empirical linear prediction (HELP) toolbox. Gaithersburg, MD: National Bureau of Standards, 1999. http://dx.doi.org/10.6028/nist.tn.1428.

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Howett, Gerald L. Linear opponent-colors model optimized for brightness prediction. Gaithersburg, MD: National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.ir.85-3202.

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Lam, H. M. Forecast of geomagnetic activity by linear prediction filtering. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1985. http://dx.doi.org/10.4095/315226.

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Dinda, Peter A., and David R. O'Hallaron. An Evaluation of Linear Models for Host Load Prediction. Fort Belvoir, VA: Defense Technical Information Center, November 1998. http://dx.doi.org/10.21236/ada358577.

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Hart, Roland J., and Stephen C. Bradshaw. Interactivity Theory: Analyzing Human Environments Using Linear Prediction Filters. Fort Belvoir, VA: Defense Technical Information Center, August 1985. http://dx.doi.org/10.21236/ada172066.

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Schleicher, Christine C., Christopher C. Bassler, Timothy C. Smith, and Lauren W. Hanyok. Assessment of Linear Seakeeping Performance Prediction of the R/V Melville. Fort Belvoir, VA: Defense Technical Information Center, July 2014. http://dx.doi.org/10.21236/ada612653.

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Dassanayake, Wajira, Chandimal Jayawardena, Iman Ardekani, and Hamid Sharifzadeh. Models Applied in Stock Market Prediction: A Literature Survey. Unitec ePress, March 2019. http://dx.doi.org/10.34074/ocds.12019.

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Stock market prices are intrinsically dynamic, volatile, highly sensitive, nonparametric, nonlinear, and chaotic in nature, as they are influenced by a myriad of interrelated factors. As such, stock market time series prediction is complex and challenging. Many researchers have been attempting to predict stock market price movements using various techniques and different methodological approaches. Recent literature confirms that hybrid models, integrating linear and non-linear functions or statistical and learning models, are better suited for training, prediction, and generalisation performance of stock market prices. The purpose of this review is to investigate different techniques applied in stock market price prediction with special emphasis on hybrid models.
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Demjanenko, V., and D. Satterlee. RTP Payload Format for the Mixed Excitation Linear Prediction Enhanced (MELPe) Codec. RFC Editor, March 2017. http://dx.doi.org/10.17487/rfc8130.

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Correa, Alfredo, Xavier Andrade, Alicia Welden, Jane Herriman, and Rafi Ullah. Quantum Non-Equilibrium Dynamics Prediction of Electronic Transport Coefficients in Non-Linear Regimes. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1727268.

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Yesha, Yaacov. Channel coding for code excited linear prediction (CELP) encoded speech in mobile radio applications. Gaithersburg, MD: National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5503.

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