Relevant bibliographies by topics / Semiparametric dynamics

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

Academic literature on the topic 'Semiparametric dynamics'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Semiparametric dynamics"

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Park, Byeong U., Enno Mammen, Wolfgang Härdle, and Szymon Borak. "Time Series Modelling With Semiparametric Factor Dynamics." Journal of the American Statistical Association 104, no. 485 (March 2009): 284–98. http://dx.doi.org/10.1198/jasa.2009.0105.

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Yang, Lijian, and Rolf Tschernig. "NON- AND SEMIPARAMETRIC IDENTIFICATION OF SEASONAL NONLINEAR AUTOREGRESSION MODELS." Econometric Theory 18, no. 6 (September 24, 2002): 1408–48. http://dx.doi.org/10.1017/s0266466602186075.

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Non- or semiparametric estimation and lag selection methods are proposed for three seasonal nonlinear autoregressive models of varying seasonal flexibility. All procedures are based on either local constant or local linear estimation. For the semiparametric models, after preliminary estimation of the seasonal parameters, the function estimation and lag selection are the same as nonparametric estimation and lag selection for standard models. A Monte Carlo study demonstrates good performance of all three methods. The semiparametric methods are applied to German real gross national product and UK public investment data. For these series our procedures provide evidence of nonlinear dynamics.
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Choroś-Tomczyk, Barbara, Wolfgang Karl Härdle, and Ostap Okhrin. "A semiparametric factor model for CDO surfaces dynamics." Journal of Multivariate Analysis 146 (April 2016): 151–63. http://dx.doi.org/10.1016/j.jmva.2015.09.002.

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Borak, Szymon, and Rafał Weron. "A semiparametric factor model for electricity forward curve dynamics." Journal of Energy Markets 1, no. 3 (September 2008): 3–16. http://dx.doi.org/10.21314/jem.2008.012.

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Dekker, T., P. Koster, and R. Brouwer. "Changing with the Tide: Semiparametric Estimation of Preference Dynamics." Land Economics 90, no. 4 (October 3, 2014): 717–45. http://dx.doi.org/10.3368/le.90.4.717.

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Härdle, Wolfgang K., and Piotr Majer. "Yield curve modeling and forecasting using semiparametric factor dynamics." European Journal of Finance 22, no. 12 (June 11, 2014): 1109–29. http://dx.doi.org/10.1080/1351847x.2014.926281.

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Fengler, M. R., W. K. Hardle, and E. Mammen. "A semiparametric factor model for implied volatility surface dynamics." Journal of Financial Econometrics 5, no. 2 (December 27, 2006): 189–218. http://dx.doi.org/10.1093/jjfinec/nbm005.

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Härdle, Wolfgang Karl, Nikolaus Hautsch, and Andrija Mihoci. "Modelling and forecasting liquidity supply using semiparametric factor dynamics." Journal of Empirical Finance 19, no. 4 (September 2012): 610–25. http://dx.doi.org/10.1016/j.jempfin.2012.04.002.

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Härdle, Wolfgang Karl, and Elena Silyakova. "Implied basket correlation dynamics." Statistics & Risk Modeling 33, no. 1-2 (January 1, 2016): 1–20. http://dx.doi.org/10.1515/strm-2014-1176.

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AbstractEquity basket correlation can be estimated both using the physical measure from stock prices, and also using the risk neutral measure from option prices. The difference between the two estimates motivates a so-called “dispersion strategy”. We study the performance of this strategy on the German market and propose several profitability improvement schemes based on implied correlation (IC) forecasts. Modelling IC conceals several challenges. Firstly the number of correlation coefficients would grow with the size of the basket. Secondly, IC is not constant over maturities and strikes. Finally, IC changes over time. We reduce the dimensionality of the problem by assuming equicorrelation. The IC surface (ICS) is then approximated from the implied volatilities of stocks and the implied volatility of the basket. To analyze the dynamics of the ICS we employ a dynamic semiparametric factor model.
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Hu, Yingyao, Robert Moffitt, and Yuya Sasaki. "Semiparametric estimation of the canonical permanent‐transitory model of earnings dynamics." Quantitative Economics 10, no. 4 (2019): 1495–536. http://dx.doi.org/10.3982/qe1117.

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This paper presents identification and estimation results for a flexible state space model. Our modification of the canonical model allows the permanent component to follow a unit root process and the transitory component to follow a semiparametric model of a higher‐order autoregressive‐moving‐average (ARMA) process. Using panel data of observed earnings, we establish identification of the nonparametric joint distributions for each of the permanent and transitory components over time. We apply the identification and estimation method to the earnings dynamics of U.S. men using the Panel Survey of Income Dynamics (PSID). The results show that the marginal distributions of permanent and transitory earnings components are more dispersed, more skewed, and have fatter tails than the normal and that earnings mobility is much lower than for the normal. We also find strong evidence for the existence of higher‐order ARMA processes in the transitory component, which lead to much different estimates of the distributions of and earnings mobility in the permanent component, implying that misspecification of the process for transitory earnings can affect estimated distributions of the permanent component and estimated earnings dynamics of that component. Thus our flexible model implies earnings dynamics for U.S. men different from much of the prior literature.
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Dissertations / Theses on the topic "Semiparametric dynamics"

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Silveira, Neto Paulo Corrêa da. "Utilização de cópulas com dinâmica semiparamétrica para estimação de medidas de risco de mercado." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/147464.

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A análise de risco de mercado, o risco associado a perdas financeiras resultantes de utilizações de preços de mercado, é fundamental para instituições financeiras e gestores de carteiras. A alocação dos ativos nas carteiras envolve decisões risco/retorno eficientes, frequentemente limitadas por uma política de risco. Muitos modelos tradicionais simplificam a estimação do risco de mercado impondo muitas suposições, como distribuições simétricas, correlações lineares, normalidade, entre outras. A utilização de cópulas exibiliza a estimação da estrutura de dependência dessas séries de tempo, possibilitando a modelagem de séries de tempo multivariadas em dois passos: estimações marginais e da dependência entre as séries. Neste trabalho, utilizou-se um modelo de cópulas com dinâmica semiparamétrica para medição de risco de mercado. A estrutura dinâmica das cópulas conta com um parâmetro de dependência que varia ao longo do tempo, em que a proposta semiparamétrica possibilita a modelagem de qualquer tipo de forma funcional que a estrutura dinâmica venha a apresentar. O modelo proposto por Hafner e Reznikova (2010), de dinâmica semiparamétrica, é comparado com o modelo sugerido por Patton (2006), que apresenta dinâmica paramétrica. Todas as cópulas no trabalho são bivariadas. Os dados consistem em quatro séries de tempo do mercado brasileiro de ações. Para cada um desses pares, utilizou-se modelos ARMA-GARCH para a estimação das marginais, enquanto a dependência entre as séries foi estimada utilizando os dois modelos de cópulas dinâmicas mencionados. Para comparar as metodologias estimaram-se duas medidas de risco de mercado: Valor em Risco e Expected Shortfall. Testes de hipóteses foram implementados para verificar a qualidade das estimativas de risco.
Market risk management, i.e. managing the risk associated with nancial loss resulting from market price uctuations, is fundamental to nancial institutions and portfolio managers. Allocations involve e cient risk/return decisions, often restricted by an investment policy statement. Many traditional models simplify risk estimation imposing several assumptions, like symmetrical distributions, the existence of only linear correlations, normality, among others. The modelling of the dependence structure of these time series can be exibly achieved by using copulas. This approach can model a complex multivariate time series structure by analyzing the problem in two blocks: marginal distributions estimation and dependence estimation. The dynamic structure of these copulas can account for a dependence parameter that changes over time, whereas the semiparametric option makes it possible to model any kind of functional form in the dynamic structure. We compare the model suggested by Hafner and Reznikova (2010), which is a dynamic semiparametric one, with the model suggested by Patton (2006), which is also dynamic but fully parametric. The copulas in this work are all bivariate. The data consists of four Brazilian stock market time series. For each of these pairs, ARMA-GARCH models have been used to model the marginals, while the dependences between the series are modeled by using the two methods mentioned above. For the comparison between these methodologies, we estimate Value at Risk and Expected Shortfall of the portfolios built for each pair of assets. Hypothesis tests are implemented to verify the quality of the risk estimates.
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Borak, Szymon. "Dynamic semiparametric factor models." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2008. http://dx.doi.org/10.18452/15802.

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Hochdimensionale Regressionsprobleme, die sich dynamisch entwickeln, sind in zahlreichen Bereichen der Wissenschaft anzutreffen. Die Dynamik eines solchen komplexen Systems wird typischerweise mittels der Zeitreiheneigenschaften einer geringen Anzahl von Faktoren analysiert. Diese Faktoren wiederum sind mit zeitinvarianten Funktionen von explikativen Variablen bewichtet. Diese Doktorarbeit beschäftigt sich mit einem dynamischen semiparametrischen Faktormodell, dass nichtparametrische Bewichtungsfunktionen benutzt. Zu Beginn sollen kurz die wichtigsten statistischen Methoden diskutiert werden um dann auf die Eigenschaften des verwendeten Modells einzugehen. Im Anschluss folgt die Diskussion einiger Anwendungen des Modellrahmens auf verschiedene Datensätze. Besondere Aufmerksamkeit wird auf die Dynamik der so genannten Implizierten Volatilität und das daraus resultierende Faktor-Hedging von Barrier Optionen gerichtet.
High-dimensional regression problems which reveal dynamic behavior occur frequently in many different fields of science. The dynamics of the whole complex system is typically analyzed by time propagation of few number of factors, which are loaded with time invariant functions of exploratory variables. In this thesis we consider dynamic semiparametric factor model, which assumes nonparametric loading functions. We start with a short discussion of related statistical techniques and present the properties of the model. Additionally real data applications are discussed with particular focus on implied volatility dynamics and resulting factor hedging of barrier options.
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Song, Song. "Confidence bands in quantile regression and generalized dynamic semiparametric factor models." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2010. http://dx.doi.org/10.18452/16341.

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In vielen Anwendungen ist es notwendig, die stochastische Schwankungen der maximalen Abweichungen der nichtparametrischen Schätzer von Quantil zu wissen, zB um die verschiedene parametrische Modelle zu überprüfen. Einheitliche Konfidenzbänder sind daher für nichtparametrische Quantil Schätzungen der Regressionsfunktionen gebaut. Die erste Methode basiert auf der starken Approximation der empirischen Verfahren und Extremwert-Theorie. Die starke gleichmäßige Konsistenz liegt auch unter allgemeinen Bedingungen etabliert. Die zweite Methode beruht auf der Bootstrap Resampling-Verfahren. Es ist bewiesen, dass die Bootstrap-Approximation eine wesentliche Verbesserung ergibt. Der Fall von mehrdimensionalen und diskrete Regressorvariablen wird mit Hilfe einer partiellen linearen Modell behandelt. Das Verfahren wird mithilfe der Arbeitsmarktanalysebeispiel erklärt. Hoch-dimensionale Zeitreihen, die nichtstationäre und eventuell periodische Verhalten zeigen, sind häufig in vielen Bereichen der Wissenschaft, zB Makroökonomie, Meteorologie, Medizin und Financial Engineering, getroffen. Der typische Modelierungsansatz ist die Modellierung von hochdimensionalen Zeitreihen in Zeit Ausbreitung der niedrig dimensionalen Zeitreihen und hoch-dimensionale zeitinvarianten Funktionen über dynamische Faktorenanalyse zu teilen. Wir schlagen ein zweistufiges Schätzverfahren. Im ersten Schritt entfernen wir den Langzeittrend der Zeitreihen durch Einbeziehung Zeitbasis von der Gruppe Lasso-Technik und wählen den Raumbasis mithilfe der funktionalen Hauptkomponentenanalyse aus. Wir zeigen die Eigenschaften dieser Schätzer unter den abhängigen Szenario. Im zweiten Schritt erhalten wir den trendbereinigten niedrig-dimensionalen stochastischen Prozess (stationär).
In many applications it is necessary to know the stochastic fluctuation of the maximal deviations of the nonparametric quantile estimates, e.g. for various parametric models check. Uniform confidence bands are therefore constructed for nonparametric quantile estimates of regression functions. The first method is based on the strong approximations of the empirical process and extreme value theory. The strong uniform consistency rate is also established under general conditions. The second method is based on the bootstrap resampling method. It is proved that the bootstrap approximation provides a substantial improvement. The case of multidimensional and discrete regressor variables is dealt with using a partial linear model. A labor market analysis is provided to illustrate the method. High dimensional time series which reveal nonstationary and possibly periodic behavior occur frequently in many fields of science, e.g. macroeconomics, meteorology, medicine and financial engineering. One of the common approach is to separate the modeling of high dimensional time series to time propagation of low dimensional time series and high dimensional time invariant functions via dynamic factor analysis. We propose a two-step estimation procedure. At the first step, we detrend the time series by incorporating time basis selected by the group Lasso-type technique and choose the space basis based on smoothed functional principal component analysis. We show properties of this estimator under the dependent scenario. At the second step, we obtain the detrended low dimensional stochastic process (stationary).
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Fritz, Marlon [Verfasser]. "Empirical analysis of dynamic macroeconomic growth and business cycle processes - using modern non- and semiparametric approaches - / Marlon Fritz." Paderborn : Universitätsbibliothek, 2019. http://d-nb.info/1191831043/34.

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Song, Song [Verfasser], Wolfgang [Akademischer Betreuer] Härdle, and Ya'acov [Akademischer Betreuer] Ritov. "Confidence bands in quantile regression and generalized dynamic semiparametric factor models / Song Song. Gutachter: Wolfgang Karl Härdle ; Ya’acov Ritov." Berlin : Humboldt Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2010. http://d-nb.info/1015129803/34.

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Tencaliec, Patricia. "Developments in statistics applied to hydrometeorology : imputation of streamflow data and semiparametric precipitation modeling." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM006/document.

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Les précipitations et les débits des cours d'eau constituent les deux variables hydrométéorologiques les plus importantes pour l'analyse des bassins versants. Ils fournissent des informations fondamentales pour la gestion intégrée des ressources en eau, telles que l’approvisionnement en eau potable, l'hydroélectricité, les prévisions d'inondations ou de sécheresses ou les systèmes d'irrigation.Dans cette thèse de doctorat sont abordés deux problèmes distincts. Le premier prend sa source dans l’étude des débits des cours d’eau. Dans le but de bien caractériser le comportement global d'un bassin versant, de longues séries temporelles de débit couvrant plusieurs dizaines d'années sont nécessaires. Cependant les données manquantes constatées dans les séries représentent une perte d'information et de fiabilité, et peuvent entraîner une interprétation erronée des caractéristiques statistiques des données. La méthode que nous proposons pour aborder le problème de l'imputation des débits se base sur des modèles de régression dynamique (DRM), plus spécifiquement, une régression linéaire multiple couplée à une modélisation des résidus de type ARIMA. Contrairement aux études antérieures portant sur l'inclusion de variables explicatives multiples ou la modélisation des résidus à partir d'une régression linéaire simple, l'utilisation des DRMs permet de prendre en compte les deux aspects. Nous appliquons cette méthode pour reconstruire les données journalières de débit à huit stations situées dans le bassin versant de la Durance (France), sur une période de 107 ans. En appliquant la méthode proposée, nous parvenons à reconstituer les débits sans utiliser d'autres variables explicatives. Nous comparons les résultats de notre modèle avec ceux obtenus à partir d'un modèle complexe basé sur les analogues et la modélisation hydrologique et d'une approche basée sur le plus proche voisin. Dans la majorité des cas, les DRMs montrent une meilleure performance lors de la reconstitution de périodes de données manquantes de tailles différentes, dans certains cas pouvant allant jusqu'à 20 ans.Le deuxième problème que nous considérons dans cette thèse concerne la modélisation statistique des quantités de précipitations. La recherche dans ce domaine est actuellement très active car la distribution des précipitations exhibe une queue supérieure lourde et, au début de cette thèse, il n'existait aucune méthode satisfaisante permettant de modéliser toute la gamme des précipitations. Récemment, une nouvelle classe de distribution paramétrique, appelée distribution généralisée de Pareto étendue (EGPD), a été développée dans ce but. Cette distribution exhibe une meilleure performance, mais elle manque de flexibilité pour modéliser la partie centrale de la distribution. Dans le but d’améliorer la flexibilité, nous développons, deux nouveaux modèles reposant sur des méthodes semiparamétriques.Le premier estimateur développé transforme d'abord les données avec la distribution cumulative EGPD puis estime la densité des données transformées en appliquant un estimateur nonparamétrique par noyau. Nous comparons les résultats de la méthode proposée avec ceux obtenus en appliquant la distribution EGPD paramétrique sur plusieurs simulations, ainsi que sur deux séries de précipitations au sud-est de la France. Les résultats montrent que la méthode proposée se comporte mieux que l'EGPD, l’erreur absolue moyenne intégrée (MIAE) de la densité étant dans tous les cas presque deux fois inférieure.Le deuxième modèle considère une distribution EGPD semiparamétrique basée sur les polynômes de Bernstein. Plus précisément, nous utilisons un mélange creuse de densités béta. De même, nous comparons nos résultats avec ceux obtenus par la distribution EGPD paramétrique sur des jeux de données simulés et réels. Comme précédemment, le MIAE de la densité est considérablement réduit, cet effet étant encore plus évident à mesure que la taille de l'échantillon augmente
Precipitation and streamflow are the two most important meteorological and hydrological variables when analyzing river watersheds. They provide fundamental insights for water resources management, design, or planning, such as urban water supplies, hydropower, forecast of flood or droughts events, or irrigation systems for agriculture.In this PhD thesis we approach two different problems. The first one originates from the study of observed streamflow data. In order to properly characterize the overall behavior of a watershed, long datasets spanning tens of years are needed. However, the quality of the measurement dataset decreases the further we go back in time, and blocks of data of different lengths are missing from the dataset. These missing intervals represent a loss of information and can cause erroneous summary data interpretation or unreliable scientific analysis.The method that we propose for approaching the problem of streamflow imputation is based on dynamic regression models (DRMs), more specifically, a multiple linear regression with ARIMA residual modeling. Unlike previous studies that address either the inclusion of multiple explanatory variables or the modeling of the residuals from a simple linear regression, the use of DRMs allows to take into account both aspects. We apply this method for reconstructing the data of eight stations situated in the Durance watershed in the south-east of France, each containing daily streamflow measurements over a period of 107 years. By applying the proposed method, we manage to reconstruct the data without making use of additional variables, like other models require. We compare the results of our model with the ones obtained from a complex approach based on analogs coupled to a hydrological model and a nearest-neighbor approach, respectively. In the majority of cases, DRMs show an increased performance when reconstructing missing values blocks of various lengths, in some of the cases ranging up to 20 years.The second problem that we approach in this PhD thesis addresses the statistical modeling of precipitation amounts. The research area regarding this topic is currently very active as the distribution of precipitation is a heavy-tailed one, and at the moment, there is no general method for modeling the entire range of data with high performance. Recently, in order to propose a method that models the full-range precipitation amounts, a new class of distribution called extended generalized Pareto distribution (EGPD) was introduced, specifically with focus on the EGPD models based on parametric families. These models provide an improved performance when compared to previously proposed distributions, however, they lack flexibility in modeling the bulk of the distribution. We want to improve, through, this aspect by proposing in the second part of the thesis, two new models relying on semiparametric methods.The first method that we develop is the transformed kernel estimator based on the EGPD transformation. That is, we propose an estimator obtained by, first, transforming the data with the EGPD cdf, and then, estimating the density of the transformed data by applying a nonparametric kernel density estimator. We compare the results of the proposed method with the ones obtained by applying EGPD on several simulated scenarios, as well as on two precipitation datasets from south-east of France. The results show that the proposed method behaves better than parametric EGPD, the MIAE of the density being in all the cases almost twice as small.A second approach consists of a new model from the general EGPD class, i.e., we consider a semiparametric EGPD based on Bernstein polynomials, more specifically, we use a sparse mixture of beta densities. Once again, we compare our results with the ones obtained by EGPD on both simulated and real datasets. As before, the MIAE of the density is considerably reduced, this effect being even more obvious as the sample size increases
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Hobert, Anne [Verfasser], Axel [Akademischer Betreuer] Munk, Axel [Gutachter] Munk, and Tatyana [Gutachter] Krivobokova. "Semiparametric Estimation of Drift, Rotation and Scaling in Sparse Sequential Dynamic Imaging: Asymptotic theory and an application in nanoscale fluorescence microscopy / Anne Hobert ; Gutachter: Axel Munk, Tatyana Krivobokova ; Betreuer: Axel Munk." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2019. http://d-nb.info/1203875312/34.

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Kolar, Mladen. "Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/229.

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Extracting knowledge and providing insights into complex mechanisms underlying noisy high-dimensional data sets is of utmost importance in many scientific domains. Statistical modeling has become ubiquitous in the analysis of high dimensional functional data in search of better understanding of cognition mechanisms, in the exploration of large-scale gene regulatory networks in hope of developing drugs for lethal diseases, and in prediction of volatility in stock market in hope of beating the market. Statistical analysis in these high-dimensional data sets is possible only if an estimation procedure exploits hidden structures underlying data. This thesis develops flexible estimation procedures with provable theoretical guarantees for uncovering unknown hidden structures underlying data generating process. Of particular interest are procedures that can be used on high dimensional data sets where the number of samples n is much smaller than the ambient dimension p. Learning in high-dimensions is difficult due to the curse of dimensionality, however, the special problem structure makes inference possible. Due to its importance for scientific discovery, we put emphasis on consistent structure recovery throughout the thesis. Particular focus is given to two important problems, semi-parametric estimation of networks and feature selection in multi-task learning.
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Borak, Szymon [Verfasser]. "Dynamic semiparametric factor models / von Szymon Borak." 2008. http://d-nb.info/990911543/34.

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Huang, Shih-Feng, and 黃士峰. "Financial Derivatives Pricing and Hedging - A Dynamic Semiparametric Approach." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/yuh4k2.

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博士
國立中山大學
應用數學系研究所
96
A dynamic semiparametric pricing method is proposed for financial derivatives including European and American type options and convertible bonds. The proposed method is an iterative procedure which uses nonparametric regression to approximate derivative values and parametric asset models to derive the continuation values. Extension to higher dimensional option pricing is also developed, in which the dependence structure of financial time series is modeled by copula functions. In the simulation study, we valuate one dimensional American options, convertible bonds and multi-dimensional American geometric average options and max options. The considered one-dimensional underlying asset models include the Black-Scholes, jump-diffusion, and nonlinear asymmetric GARCH models and for multivariate case we study copula models such as the Gaussian, Clayton and Gumbel copulae. Convergence of the method is proved under continuity assumption on the transition densities of the underlying asset models. And the orders of the supnorm errors are derived. Both the theoretical findings and the simulation results show the proposed approach to be tractable for numerical implementation and provides a unified and accurate technique for financial derivative pricing. The second part of this thesis studies the option pricing and hedging problems for conditional leptokurtic returns which is an important feature in financial data. The risk-neutral models for log and simple return models with heavy-tailed innovations are derived by an extended Girsanov change of measure, respectively. The result is applicable to the option pricing of the GARCH model with t innovations (GARCH-t) for simple eturn series. The dynamic semiparametric approach is extended to compute the option prices of conditional leptokurtic returns. The hedging strategy consistent with the extended Girsanov change of measure is constructed and is shown to have smaller cost variation than the commonly used delta hedging under the risk neutral measure. Simulation studies are also performed to show the effect of using GARCH-normal models to compute the option prices and delta hedging of GARCH-t model for plain vanilla and exotic options. The results indicate that there are little pricing and hedging differences between the normal and t innovations for plain vanilla and Asian options, yet significant disparities arise for barrier and lookback options due to improper distribution setting of the GARCH innovations.
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Book chapters on the topic "Semiparametric dynamics"

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Deco, Gustavo, and Bernd Schürmann. "Applications: Semiparametric Characterization of Time Series." In Information Dynamics, 181–203. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0127-1_8.

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Deco, Gustavo, and Bernd Schürmann. "Statistical Structure Extraction in Dynamical Systems: Semiparametric Formulation." In Information Dynamics, 165–80. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0127-1_7.

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Hirano, Keisuke. "A Semiparametric Model for Labor Earnings Dynamics." In Practical Nonparametric and Semiparametric Bayesian Statistics, 355–69. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1732-9_20.

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Kozumi, Hideo, and Wolfgang Polasek. "A Bayesian Semiparametric Analysis of ARCH Models." In Optimization, Dynamics, and Economic Analysis, 389–400. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57684-3_33.

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Fahrmeir, Ludwig, and Leonhard Knorr-Held. "Dynamic and Semiparametric Models." In Smoothing and Regression, 513–44. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118150658.ch18.

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Hautsch, Nikolaus. "Semiparametric Dynamic Proportional Hazard Models." In Econometrics of Financial High-Frequency Data, 245–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21925-2_10.

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Hautsch, Nikolaus. "Semiparametric Dynamic Proportional Intensity Models." In Lecture Notes in Economics and Mathematical Systems, 159–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17015-7_6.

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Basile, Roberto, Saime Kayam, Román Mínguez, Jose María Montero, and Jesús Mur. "Semiparametric Spatial Autoregressive Geoadditive Models." In Dynamic Modeling and Econometrics in Economics and Finance, 73–98. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12805-4_4.

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Heikkinen, Juha. "Curve and Surface Estimation Using Dynamic Step Functions." In Practical Nonparametric and Semiparametric Bayesian Statistics, 255–72. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1732-9_14.

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Doss, Hani, and B. Narasimhan. "Dynamic Display of Changing Posterior in Bayesian Survival Analysis." In Practical Nonparametric and Semiparametric Bayesian Statistics, 63–87. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1732-9_4.

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Conference papers on the topic "Semiparametric dynamics"

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Camoriano, Raffaello, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, and Francesco Nori. "Incremental semiparametric inverse dynamics learning." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487177.

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Romeres, Diego, Devesh K. Jha, Alberto DallaLibera, Bill Yerazunis, and Daniel Nikovski. "Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8794229.

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Reports on the topic "Semiparametric dynamics"

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Li, Degui, Jia Chen, Oliver Linton, and Zudi Lu. Semiparametric dynamic portfolio choice with multiple conditioning variables. IFS, February 2015. http://dx.doi.org/10.1920/wp.cem.2015.0715.

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Bajari, Patrick, and Han Hong. Semiparametric Estimation of a Dynamic Game of Incomplete Information. Cambridge, MA: National Bureau of Economic Research, February 2006. http://dx.doi.org/10.3386/t0320.

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Bajari, Patrick, Victor Chernozhukov, Han Hong, and Denis Nekipelov. Identification and Efficient Semiparametric Estimation of a Dynamic Discrete Game. Cambridge, MA: National Bureau of Economic Research, April 2015. http://dx.doi.org/10.3386/w21125.

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