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Relevant bibliographies by topics / Smoothing

Academic literature on the topic 'Smoothing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 June 2024

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Journal articles on the topic "Smoothing"

1

Lee, Uk-Jae, Byeong Wook Lee, Dong-Hui Ko, and Hong-Yeon Cho. "Optimal Estimation of the Peak Wave Period using Smoothing Method." Journal of Korean Society of Coastal and Ocean Engineers 34, no. 6 (December 27, 2022): 266–74. http://dx.doi.org/10.9765/kscoe.2022.34.6.266.

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In this study, a smoothing method was applied to improve the accuracy of peak wave period estimation using the water surface elevation observed from the Oceanographic and Meteorological Observation Tower located on the west coast of the Korean Peninsula. Validation of the application of the smoothing method was performed using variance of the surface elevation and total amount wave energy, and then the effect on the application of smoothing was analyzed. As a result of the analysis, the correlation coefficient between variance of the surface elevation and total amount wave energy was 0.9994, confirming that there was no problem in applying the method. Thereafter, as a result of reviewing the effect of smoothing, it was found to be reduced by about 4 times compared to the confidence interval of the existing estimated spectrum, confirming that the accuracy of the estimated peak wave period was improved. It was found that there was a statistically significant difference in probability density between 4 and 6 seconds due to the smoothing application. In addition, for optimal smoothing, the appropriate number of smoothings according to the significant wave height range was calculated using a statistical technique, and the number of smoothings was found to increase due to the unstable spectral shape as the significant wave height decreased.

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Baszczyńska, Aleksandra Katarzyna. "One Value of Smoothing Parameter vs Interval of Smoothing Parameter Values in Kernel Density Estimation." Acta Universitatis Lodziensis. Folia Oeconomica 6, no. 332 (February 2, 2018): 73–86. http://dx.doi.org/10.18778/0208-6018.332.05.

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Ad hoc methods in the choice of smoothing parameter in kernel density estimation, although often used in practice due to their simplicity and hence the calculated efficiency, are characterized by quite big error. The value of the smoothing parameter chosen by Silverman method is close to optimal value only when the density function in population is the normal one. Therefore, this method is mainly used at the initial stage of determining a kernel estimator and can be used only as a starting point for further exploration of the smoothing parameter value. This paper presents ad hoc methods for determining the smoothing parameter. Moreover, the interval of smoothing parameter values is proposed in the estimation of kernel density function. Basing on the results of simulation studies, the properties of smoothing parameter selection methods are discussed.

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Livingston, Charles. "Chiral smoothings of knots." Proceedings of the Edinburgh Mathematical Society 63, no. 4 (November 2020): 1048–61. http://dx.doi.org/10.1017/s0013091520000322.

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AbstractCan smoothing a single crossing in a diagram for a knot convert it into a diagram of the knot's mirror image? Zeković found such a smoothing for the torus knot T(2, 5), and Moore–Vazquez proved that such smoothings do not exist for other torus knots T(2, m) with m odd and square free. The existence of such a smoothing implies that K # K bounds a Mobius band in B4. We use Casson–Gordon theory to provide new obstructions to the existence of such chiral smoothings. In particular, we remove the constraint that m be square free in the Moore–Vazquez theorem, with the exception of m = 9, which remains an open case. Heegaard Floer theory provides further obstructions; these do not give new information in the case of torus knots of the form T(2, m), but they do provide strong constraints for other families of torus knots. A more general question asks, for each pair of knots K and J, what is the minimum number of smoothings that are required to convert a diagram of K into one for J. The methods presented here can be applied to provide lower bounds on this number.

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Morduch, Jonathan. "Income Smoothing and Consumption Smoothing." Journal of Economic Perspectives 9, no. 3 (August 1, 1995): 103–14. http://dx.doi.org/10.1257/jep.9.3.103.

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One way that risk-averse households protect consumption levels is to borrow and use insurance mechanisms. Another way, common in low-income economies, is to diversify economic activities and make conservative production and employment choices. Households thus tend toward limiting exposure only to shocks that can be handled with available credit and insurance. Typically, both types of mechanisms are studied independently but much more can be learned by studying them together. First, we obtain a more complete picture of risks, costs, and insurance possibilities. Second, it opens the way to considering biases in standard tests of credit and insurance.

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Harianto, Syawal, Haris Al Amin, and Yusmika Indah. "Pengaruh Ukuran Perusahaan, dan Leverange Terhadap Praktik Income Smoothing pada Bank Syariah." Jurnal EMT KITA 4, no. 1 (September 10, 2020): 80. http://dx.doi.org/10.35870/emt.v4i2.136.

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This research is to know the effect of firm size, and financial leverage to Income Smoothing practices is Islamic Banks. The data used is the secondary data with sourced from annual report data published by Islamic commercial banks and syariah business unit during 2016-2018 periods, samples research are 54 (fifty four) bank. Data analysis method using eviews with the fixed effect model. The result of the research shows that the simultan firm size and financial leverage have significant effect on Income Smoothing in Islamic banks.the partially, firm size an financial leverage has a positive and significant effect on income smoothin practices in Islamic banks City. The determination test result is 55%. Keywords: Firm Size, Financial leverage, Income Smoothing.

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Wang, X., P. Du, and J. Shen. "Smoothing splines with varying smoothing parameter." Biometrika 100, no. 4 (August 30, 2013): 955–70. http://dx.doi.org/10.1093/biomet/ast031.

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Turlach, Berwin A. "Shape constrained smoothing using smoothing splines." Computational Statistics 20, no. 1 (March 2005): 81–104. http://dx.doi.org/10.1007/bf02736124.

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Purwaningsih, Endang, and Oktofiana Busa Taran Wanan. "PENGARUH UKURAN PERUSAHAAN, FINANCIAL LEVERAGE, STRUKTUR KEPEMILIKAN, CASH HOLDING, REPUTASI AUDITOR TERHADAP INCOME SMOOTHING (STUDI EMPIRIS PERUSAHAAN MANUFAKTUR TERDAFTAR DI BEI PERIODE 2018 - 2020)." Media Akuntansi 34, no. 01 (June 29, 2022): 063–74. http://dx.doi.org/10.47202/mak.v34i01.155.

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This study aims to determine the effect ofcompany size, financial leverage,ownership structure, cash holdings,auditor's reputation for income smoothingin manufacturing sector companies listed on the Indonesia Stock Exchange (IDX) for the 2018-2020 period. The sampling technique used secondary data sources, namely data obtained or collected by researchers from various existing sources. The population used in this study were 193 manufacturing companies with a total sample of 40 companies. The analytical method used is the classical assumption test and multiple regression test.Based on the analysis conducted, it can be concluded that this study meets the requirements of the classical assumption test. Hypothesis testing using t test shows that firm size has a significant positive effect on income smoothing with a sig value of 2.6%, financial leverage has a significant negative effect on income smoothing with a sig value of 3.0%, managerial ownership has a significantpositive effect on income smoothing with a sig value of 1.3 %, institutional ownership has a significant negative effect on income smoothing with a sig value of 1.8%, cash holding a significantly negative effect on income smoothing with a sig value of 3.8%, and auditor reputation has a significant positive effect on income smoothing with a sig value of 4.6%.The results of the f test together with independent variables including company size, financial leverage, managerial ownership, institutional ownership, cash holding and auditor reputation have a significant effect on income smoothing. The value of the coefficient of determination (Adjusted R2) from the equation tested in this study was 42.8%. This indicates that income smoothing is only explained by 42.8% by company size, financial leverage, managerial ownership, institutional ownership, cash holding and auditor reputation while the rest is explained by other variables outside the equation.

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Malik, Amina, Haroon Aziz, Buerhan Saiti, and Shahab Ud Din. "The Impact of Earnings variability and Regulatory Measures on Income Smoothing: Evidence from Panel Regression." Journal of Central Banking Theory and Practice 10, no. 1 (January 1, 2021): 183–201. http://dx.doi.org/10.2478/jcbtp-2021-0009.

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Abstract This study investigates the impact of variability in earnings, stringent regulatory measures and the trend of extending loans while keeping in view deposit ratio on income smoothening practices for a sample of 20 commercial banks listed on the Pakistan Stock Exchange (PSX) from the year 2010 to 2017. The likelihood of smoothing activities is measured through its widely used proxy, i.e. loan loss provisions (LLPs). Moreover, earnings before tax and provisions (EBTP) and loan to deposit ratio (LD) have been incorporated to determine the impact of earnings and loans to deposit ratio on income smoothening. We find that commercial banks are less likely to manage earnings through smoothening practices, which shows that commercial banks adhere to regulatory restrictions. This is further supported by the fact that income smoothing activities decrease as a result of the increase in capital adequacy ratios after the imposition of stringent rules, which exert greater regulatory pressure on banks, whereas the pace of income smoothing increases as a result of an increase in loans to deposit ratio, which reveals that banks take credit risk but manage within the ambit of regulatory restrictions. Based on the findings, we argue that the imposition of regulatory restrictions through the State Bank of Pakistan (SBP) has not only discouraged income smoothening through loan loss provisions but also enhances reporting quality. The results of this study provide useful insights for investors, creditors and stakeholders.

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MUNTEANU, FLORIN, CRISTIAN IOANA, CRISTIAN ŞUŢEANU, and EDMOND CREŢU. "SMOOTHING DIMENSIONS FOR TIME SERIES CHARACTERIZATION." Fractals 03, no. 02 (June 1995): 315–28. http://dx.doi.org/10.1142/s0218348x95000254.

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The paper refers to a recently introduced fractal signal analysis method, which relies on scaling properties of signal parameters regarding the cutoff frequency used to smoothen the signal’s graph. The relations between the smoothing dimensions and other exponents (fractal dimension df, power spectrum exponent ß, Hurst exponent H) are determined theoretically and tested by numerical experiments.

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Dissertations / Theses on the topic "Smoothing"

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Lawton, Richard. "Exponential smoothing methods." Thesis, University of Bath, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340928.

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Svetunkov, Ivan. "Complex exponential smoothing." Thesis, Lancaster University, 2016. http://eprints.lancs.ac.uk/82749/.

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Exponential smoothing is one of the most popular forecasting methods in practice. It has been used and researched for more than half a century. It started as an ad-hoc forecasting method and developed to a family of state-space models. Still all exponential smoothing methods are based on time series decomposition and the usage of such components as "level", "trend", "seasonality" and "error". It is assumed that these components may vary from one time series to another and take different forms depending on data characteristics. This makes their definition arbitrary and in fact there is no single way of identifying these components. At the same time the introduction of different types of exponential smoothing components implies that a model selection procedure is needed. This means that a researcher needs to select an appropriate type of model out of 30 different types either manually or automatically for every time series analysed. However several recent studies show that an underlying statistical model may have a form completely different than the one assumed by specific exponential smoothing models. These modelling questions motivate our research. We propose a model without strictly defined "level", "trend" and "seasonality". The model greatly simplifies the selection procedure, distinguishing only between seasonal and non-seasonal time series. Although we call it "Complex Exponential Smoothing" (CES), due to the use of complex-valued functions, its usage simplifies the forecasting procedure. In this thesis we first discuss the main properties of CES and propose an underlying statistical model. We then extend it in order to take seasonality into account and conduct experiments on real data to compare its performance with several well-known univariate forecasting models. We proceed to discuss the parameters estimation for exponential smoothing and propose a "Trace Forecast Likelihood" function that allows estimating CES components more efficiently. Finally we show that Trace Forecast Likelihood has desirable statistical properties, is connected to shrinkage and is generally advisable to use with any univariate model.

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Guevara, Prieto Ivonne Alexandra. "Quantum State Smoothing." Thesis, Griffith University, 2016. http://hdl.handle.net/10072/367795.

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Smoothing is an estimation method whereby a classical state (probability distribution for classical variables) at a given time is conditioned on all-time (both past and future) observations. This method has been well developed in classical systems, but its application to quantum systems has only recently begun to be explored. Previous works have used the term "quantum smoothing" to mean estimating classical parameters, (Phys. Rev. Lett., 102, 250403, (2009)), which is essentially classical smoothing in which the noisy observation of the classical parameters is mediated by a quantum system. In this thesis, I define a smoothed quantum state for a partially monitored open quantum system, conditioned on an all-time monitoring-derived record. I calculate the smoothed distribution for a hypothetical unobserved record Ut which, when added to the real record O, would complete the monitoring, yielding a pure-state "quantum trajectory". Averaging the pure state over this smoothed distribution yields the (mixed) smoothed quantum state S that is typically purer than the state F conditioned only on the past. I also study how the choice of actual unravelling affects the purity increment over that of the conventional (filtered) state conditioned only on the past record.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Natural Sciences
Science, Environment, Engineering and Technology
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Ni, Kai. "Tectonic smoothing and mapping." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41072.

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Large-scale mapping has become the key to numerous applications, e.g. simultaneous localization and mapping (SLAM) for autonomous robots. Despite of the success of many SLAM projects, there are still some challenging scenarios in which most of the current algorithms are not able to deliver an exact solution fast enough. One of these challenges is the size of SLAM problems, which has increased by several magnitudes over the last decade. Another challenge for SLAM problems is the large amount of noise baked in the measurements, which often yields poor initializations and slows or even fails the optimization. Urban 3D reconstruction is another popular application for large-scale mapping and has received considerable attention recently from the computer vision community. High-quality 3D models are useful in various successful cartographic and architectural applications, such as Google Earth or Microsoft Live Local. At the heart of urban reconstruction problems is structure from motion (SfM). Due to the wide availability of cameras, especially on handhold devices, SfM is becoming a more and more crucial technique to handle a large amount of images. In the thesis, I present a novel batch algorithm, namely Tectonic Smoothing and Mapping (TSAM). I will show that the original SLAM graph can be recursively partitioned into multiple-level submaps using the nested dissection algorithm, which leads to the cluster tree, a powerful graph representation. By employing the nested dissection algorithm, the algorithm greatly minimizes the dependencies between two subtrees, and the optimization of the original graph can be done using a bottom-up inference along the corresponding cluster tree. To speed up the computation, a base node is introduced for each submap and is used to represent the rigid transformation of the submap in the global coordinate frame. As a result, the optimization moves the base nodes rather than the actual submap variables. I will also show that TSAM can be successfully applied to the SfM problem as well, in which a hypergraph representation is employed to capture the pairwise constraints between cameras. The hierarchical partitioning based on the hypergraph not only yields a cluster tree as in the SLAM problem but also forces resulting submaps to be nonsingular. I will demonstrate the TSAM algorithm using various simulation and real-world data sets.

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Yake, Bronson Thomas. "Self-Smoothing Functional Estimation." MSSTATE, 2002. http://sun.library.msstate.edu/ETD-db/theses/available/etd-09032002-090546/.

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Analysis of measured data is often required when there is no deep understanding of the mathematics that accurately describes the process being measured. Additionally, realistic estimation of the derivative of measured data is often useful. Current techniques of accomplishing this type of data analysis are labor intensive, prone to significant error, and highly dependent on the expertise of the engineer performing the analysis. The ?Self-Smoothing Functional Estimation? (SSFE) algorithm was developed to automate the analysis of measured data and to provide a reliable basis for the extraction of derivative information. In addition to the mathematical development of the SSFE algorithm, an example is included in Chapter III that illustrates several of the innovative features of the SSFE and associated algorithms. Conclusions are drawn about the usefulness of the algorithm from an engineering perspective and additional possible uses are mentioned.

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Kaess, Michael. "Incremental smoothing and mapping." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26572.

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Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2009.
Committee Chair: Dellaert, Frank; Committee Member: Bobick, Aaron; Committee Member: Christensen, Henrik; Committee Member: Leonard, John; Committee Member: Rehg, James. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Saleh, Mojtaba. "Smoothing of Photovoltaic Generation." Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/66584.

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This study investigates the solutions to eliminate the intermittency of the photovoltaic (PV) generation resources in the electric grids. It addresses the battery storage backup as a conventional solution to absorb the sudden dips and rises in the PV generation, which are mainly attributed to passing clouds. This approach is further improved by incorporating the cloud prediction information produced by processing the real-time sky images. This proved to mitigate the reliance on the storage backup.

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Chakravarty, Subhashish. "Bayesian surface smoothing under anisotropy." Diss., University of Iowa, 2007. http://ir.uiowa.edu/etd/169.

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Simón, Colomar David, John-Olof Nilsson, and Peter Händel. "Smoothing for ZUPT-aided INSs." KTH, Signalbehandling, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-107414.

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Due to the recursive and integrative nature of zero-velocity-update-aided (ZUPT-aided) inertial navigation systems (INSs), the error covariance increases throughout each ZUPT-less period followed by a drastic decrease and large state estimate corrections as soon as ZUPTs are applied. For dead-reckoning with foot-mounted inertial sensors, this gives undesirable discontinuities in the estimated trajectory at the end of each step. However, for many applications, some degree of lag can be tolerated and the information provided by the ZUPTs at the end of a step can be made available throughout the step, eliminating the discontinuities. For this purpose, we propose a smoothing algorithm for ZUPT-aided INSs. For near real-time applications, smoothing is applied to the data in a step-wise manner requiring a suggested varying-lag segmentation rule. For complete off-line processing, full data set smoothing is examined. Finally, the consequences and impact of smoothing are analyzed and quantified based on real-data.

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Eichmann, Katrin. "Smoothing stochastic bang-bang problems." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2013. http://dx.doi.org/10.18452/16799.

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Motiviert durch das Problem der optimalen Strategie beim Handel einer großen Aktienposition, behandelt diese Arbeit ein stochastisches Kontrollproblem mit zwei besonderen Eigenschaften. Zum einen wird davon ausgegangen, dass das Kontrollproblem eine exponentielle Verzögerung in der Kontrollvariablen beinhaltet, zum anderen nehmen wir an, dass die Koeffizienten des Kontrollproblems linear in der Kontrollvariablen sind. Wir erhalten ein degeneriertes stochastisches Kontrollproblem, dessen Lösung - sofern sie existiert - Bang-Bang-Charakter hat. Die resultierende Unstetigkeit der optimalen Kontrolle führt dazu, dass die Existenz einer optimalen Lösung nicht selbstverständlich ist und bewiesen werden muss. Es wird eine Folge von stochastischen Kontrollproblemen mit Zustandsprozessen konstruiert, deren jeweilige Diffusionsmatrix invertierbar ist und die ursprüngliche degenerierte Diffusionsmatrix approximiert. Außerdem stellen die Kostenfunktionale der Folge eine konvexe Approximation des ursprünglichen linearen Kostenfunktionals dar. Um die Konvergenz der Lösungen dieser Folge zu zeigen, stellen wir die Kontrollprobleme in Form von stochastischen Vorwärts-Rückwärts-Differential-gleichungen (FBSDEs) dar. Wir zeigen, dass die zu der konstruierten Folge von Kontrollproblemen gehörigen Lösungen der Vorwärts-Rückwärtsgleichungen – zumindest für eine Teilfolge - in Verteilung konvergieren. Mit Hilfe einer Konvexitätsannahme der Koeffizienten ist es möglich, einen Kontroll-prozess auf einem passenden Wahrscheinlichkeitsraum zu konstruieren, der optimal für das ursprüngliche stochastische Kontrollproblem ist. Neben der damit bewiesenen Existenz einer optimalen (Bang-Bang-) Lösung, wird damit auch eine glatte Approximation der unstetigen Bang-Bang-Lösung erreicht, welche man für die numerische Approximation des Problems verwenden kann. Die Ergebnisse werden schließlich dann in Form von numerischen Simulationen auf das Problem der optimalen Handels¬ausführung angewendet.
Motivated by the problem of how to optimally execute a large stock position, this thesis considers a stochastic control problem with two special properties. First, the control problem has an exponential delay in the control variable, and so the present value of the state process depends on the moving average of past control decisions. Second, the coefficients are assumed to be linear in the control variable. It is shown that a control problem with these properties generates a mathematically challenging problem. Specifically, it becomes a stochastic control problem whose solution (if one exists) has a bang-bang nature. The resulting discontinuity of the optimal solution creates difficulties in proving the existence of an optimal solution and in solving the problem with numerical methods. A sequence of stochastic control problems with state processes is constructed, whose diffusion matrices are invertible and approximate the original degenerate diffusion matrix. The cost functionals of the sequence of control problems are convex approximations of the original linear cost functional. To prove the convergence of the solutions, the control problems are written in the form of forward-backward stochastic differential equations (FBSDEs). It is then shown that the solutions of the FBSDEs corresponding to the constructed sequence of control problems converge in law, at least along a subsequence. By assuming convexity of the coefficients, it is then possible to construct from this limit an admissible control process which, for an appropriate reference stochastic system, is optimal for our original stochastic control problem. In addition to proving the existence of an optimal (bang-bang) solution, we obtain a smooth approximation of the discontinuous optimal bang-bang solution, which can be used for the numerical solution of the problem. These results are then applied to the optimal execution problem in form of numerical simulations.

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Books on the topic "Smoothing"

1

Härdle, Wolfgang. Smoothing Techniques. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-4432-5.

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Wand, M. P., and M. C. Jones. Kernel Smoothing. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4493-1.

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C, Jones M., ed. Kernel smoothing. London: Chapman & Hall, 1995.

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Caballero, Ricardo J. Smoothing sudden stops. Cambridge, MA: Massachusetts Institute of Technology, Dept. of Economics, 2001.

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Hirukawa, Masayuki. Asymmetric Kernel Smoothing. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5466-2.

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Schimek, Michael G., ed. Smoothing and Regression. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2000. http://dx.doi.org/10.1002/9781118150658.

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Caballero, Ricardo J. Smoothing sudden stops. Cambridge, MA: National Bureau of Economic Research, 2001.

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Koenker, Roger W. Quantile smoothing splines. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1992.

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Koenker, Roger W. Quantile smoothing splines. [Urbana, Ill]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1991.

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Koenker, Roger W. Quantile smoothing splines. Champaign: University of Illinois at Urbana-Champaign, 1993.

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Book chapters on the topic "Smoothing"

1

Gooch, Jan W. "Smoothing." In Encyclopedic Dictionary of Polymers, 996. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15379.

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Weik, Martin H. "smoothing." In Computer Science and Communications Dictionary, 1607. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17628.

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Hillier, Frederick S., and Bennett L. Fox. "Smoothing." In Strategies for Quasi-Monte Carlo, 51–93. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5221-5_2.

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Aydede, Yigit. "Smoothing." In Machine Learning Toolbox for Social Scientists, 129–36. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003381501-9.

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Wand, M. P., and M. C. Jones. "Introduction." In Kernel Smoothing, 1–9. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4493-1_1.

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Wand, M. P., and M. C. Jones. "Univariate kernel density estimation." In Kernel Smoothing, 10–57. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4493-1_2.

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Wand, M. P., and M. C. Jones. "Bandwidth selection." In Kernel Smoothing, 58–89. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4493-1_3.

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Wand, M. P., and M. C. Jones. "Multivariate kernel density estimation." In Kernel Smoothing, 90–113. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4493-1_4.

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Wand, M. P., and M. C. Jones. "Kernel regression." In Kernel Smoothing, 114–45. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4493-1_5.

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Wand, M. P., and M. C. Jones. "Selected extra topics." In Kernel Smoothing, 146–71. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4493-1_6.

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Conference papers on the topic "Smoothing"

1

Bannister, Rose, Jeanne Paulette Bickford, and Karl Swanke. "Demand smoothing." In 2013 24th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC 2013). IEEE, 2013. http://dx.doi.org/10.1109/asmc.2013.6552774.

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Cai, L. D. "Spline Smoothing: A Special Case of Diffusion Smoothing." In Alvey Vision Conference 1989. Alvey Vision Club, 1989. http://dx.doi.org/10.5244/c.3.47.

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Wenyan, Ma, Jia Yongjie, Xiao Gang, and Qiu Xaolin. "Study of ¿ Energy Spectrum Smoothing and Appraising Smoothing Goodness." In 2007 8th International Conference on Electronic Measurement and Instruments. IEEE, 2007. http://dx.doi.org/10.1109/icemi.2007.4350555.

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4

Letchford, Adrian, Junbin Gao, and Lihong Zheng. "Smoothing Security Prices." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.188.

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5

Abadi, Aharon, Roie Melamed, Eli Packer, and Natalie Shapira. "Smoothing indoor trajectories." In 2016 International Conference on Localization and GNSS (ICL-GNSS). IEEE, 2016. http://dx.doi.org/10.1109/icl-gnss.2016.7533845.

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6

Bannerman, P. L. "Smoothing Innovation Discontinuities." In 2008 IEEE International Conference on Communications. IEEE, 2008. http://dx.doi.org/10.1109/icc.2008.1023.

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Klaas, Mike, Mark Briers, Nando de Freitas, Arnaud Doucet, Simon Maskell, and Dustin Lang. "Fast particle smoothing." In the 23rd international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1143844.1143905.

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Mattern, Christopher. "Generalized Probability Smoothing." In 2018 Data Compression Conference (DCC). IEEE, 2018. http://dx.doi.org/10.1109/dcc.2018.00033.

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Al‐Dossary, Saleh, and Kurt J. Marfurt. "Fracture‐preserving smoothing." In SEG Technical Program Expanded Abstracts 2003. Society of Exploration Geophysicists, 2003. http://dx.doi.org/10.1190/1.1817927.

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Leordeanu, Marius, and Martial Hebert. "Smoothing-based Optimization." In 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2008. http://dx.doi.org/10.1109/cvpr.2008.4587482.

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Reports on the topic "Smoothing"

1

Hundtofte, Sean, Arna Olafsson, and Michaela Pagel. Credit Smoothing. Cambridge, MA: National Bureau of Economic Research, October 2019. http://dx.doi.org/10.3386/w26354.

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2

Allen, Donald S. Seasonal Production Smoothing. Federal Reserve Bank of St. Louis, 1999. http://dx.doi.org/10.20955/wp.1999.004.

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3

Caballero, Ricardo, and Arvind Krishnamurthy. Smoothing Sudden Stops. Cambridge, MA: National Bureau of Economic Research, August 2001. http://dx.doi.org/10.3386/w8427.

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4

Baugh, Brian, Itzhak Ben-David, Hoonsuk Park, and Jonathan Parker. Asymmetric Consumption Smoothing. Cambridge, MA: National Bureau of Economic Research, September 2018. http://dx.doi.org/10.3386/w25086.

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5

Barro, Robert. Interest-Rate Smoothing. Cambridge, MA: National Bureau of Economic Research, May 1988. http://dx.doi.org/10.3386/w2581.

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6

Schoenwald, David A. PV Smoothing Duty Cycle. Office of Scientific and Technical Information (OSTI), March 2016. http://dx.doi.org/10.2172/1529051.

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7

Luettgen, Mark R., and Alan S. Willsky. Multiscale Smoothing Error Models. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada459332.

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8

West, Kenneth. Order Backlogs and Production Smoothing. Cambridge, MA: National Bureau of Economic Research, September 1987. http://dx.doi.org/10.3386/w2385.

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9

Soanes, Royce. Function Smoothing by Repeated Averaging. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada196575.

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10

Grossman, Herschel, and Taejoon Han. Sovereign Debt and Consumption Smoothing. Cambridge, MA: National Bureau of Economic Research, April 1997. http://dx.doi.org/10.3386/w5997.

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