Relevant bibliographies by topics / Semiparametric theory

Contents

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

Academic literature on the topic 'Semiparametric theory'

Author: Grafiati
Published: 30 November 2024

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Journal articles on the topic "Semiparametric theory"

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Fokianos, Konstantinos. "Semiparametric Theory and Missing Data." Technometrics 49, no. 2 (May 2007): 228–29. http://dx.doi.org/10.1198/tech.2007.s488.

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Kaji, Tetsuya. "Theory of Weak Identification in Semiparametric Models." Econometrica 89, no. 2 (2021): 733–63. http://dx.doi.org/10.3982/ecta16413.

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We provide general formulation of weak identification in semiparametric models and an efficiency concept. Weak identification occurs when a parameter is weakly regular, that is, when it is locally homogeneous of degree zero. When this happens, consistent or equivariant estimation is shown to be impossible. We then show that there exists an underlying regular parameter that fully characterizes the weakly regular parameter. While this parameter is not unique, concepts of sufficiency and minimality help pin down a desirable one. If estimation of minimal sufficient underlying parameters is inefficient, it introduces noise in the corresponding estimation of weakly regular parameters, whence we can improve the estimators by local asymptotic Rao–Blackwellization. We call an estimator weakly efficient if it does not admit such improvement. New weakly efficient estimators are presented in linear IV and nonlinear regression models. Simulation of a linear IV model demonstrates how 2SLS and optimal IV estimators are improved.
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Bouzebda, Salim, and Mohamed Cherfi. "General inference in semiparametric models through divergences and the duality technique with applications." Theory of Stochastic Processes 25(41), no. 1 (December 21, 2020): 1–24. http://dx.doi.org/10.37863/tsp-7370403638-47.

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In this paper, we extend the dual divergence approach to general semiparametric models and study dual divergence estimators for semiparametric models. Asymptotic properties such as consistency, asymptotic normality of the proposed estimators are deeply investigated by mean the sophisticated modern empirical theory. We investigate the exchangeably weighted estimators in this setting and establish the consistency. We finally consider the functional M-estimator and obtain its weak convergence result.
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Saart, Patrick W., Jiti Gao, and David E. Allen. "Semiparametric Autoregressive Conditional Duration Model: Theory and Practice." Econometric Reviews 34, no. 6-10 (December 17, 2014): 849–81. http://dx.doi.org/10.1080/07474938.2014.956594.

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XU, Fang-min, Xiao-dong XU, and Ping ZHANG. "Semiparametric theory based MIMO model and performance analysis." Journal of China Universities of Posts and Telecommunications 14, no. 4 (December 2007): 36–40. http://dx.doi.org/10.1016/s1005-8885(08)60035-7.

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Hall, Peter, and Joel L. Horowitz. "Bandwidth Selection in Semiparametric Estimation of Censored Linear Regression Models." Econometric Theory 6, no. 2 (June 1990): 123–50. http://dx.doi.org/10.1017/s0266466600005089.

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Quantile and semiparametric M estimation are methods for estimating a censored linear regression model without assuming that the distribution of the random component of the model belongs to a known parametric family. Both methods require estimating derivatives of the unknown cumulative distribution function of the random component. The derivatives can be estimated consistently using kernel estimators in the case of quantile estimation and finite difference quotients in the case of semiparametric M estimation. However, the resulting estimates of derivatives, as well as parameter estimates and inferences that depend on the derivatives, can be highly sensitive to the choice of the kernel and finite difference bandwidths. This paper discusses the theory of asymptotically optimal bandwidths for kernel and difference quotient estimation of the derivatives required for quantile and semiparametric M estimation, respectively. We do not present a fully automatic method for bandwidth selection.
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Breitung, Jörg, and Philip Hans Franses. "ON PHILLIPS–PERRON-TYPE TESTS FOR SEASONAL UNIT ROOTS." Econometric Theory 14, no. 2 (April 1998): 200–221. http://dx.doi.org/10.1017/s0266466698142032.

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In this paper we consider a semiparametric version of the test for seasonal unit roots suggested by Hylleberg, Engle, Granger, and Yoo (1990, Journal of Econometrics 44, 215–238). The asymptotic theory is based on the analysis of a simple regression problem, and the results apply to tests at any given frequency in the range (0,π]. Monte Carlo simulations suggest that the test may have more power than the parametric test of Hylleberg et al. (1990). On the other hand, the semiparametric version suffers from severe size distortions in some situations.
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Hidayati, Lilik, Nur Chamidah, and I. Nyoman Budiantara. "ESTIMASI SELANG KEPERCAYAAN NILAI UJIAN NASIONAL BERBASIS KOMPETENSI BERDASARKAN MODEL REGRESI SEMIPARAMETRIK MULTIRESPON TRUNCATED SPLINE." MEDIA STATISTIKA 13, no. 1 (June 25, 2020): 92–103. http://dx.doi.org/10.14710/medstat.13.1.92-103.

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Confidence interval estimation is important in statistical inference for the parameters of the regression model, but the theory of confidence interval estimation for multi-response semiparametric regression model parameters based on the truncated spline estimator has not been examined. In this study, we estimate the confidence interval of the multi-response semiparametric regression model based on the truncated spline estimator by using pivotal quantity method with the central limit theorem approach. This confidence interval theory is applied to data of competency-based national exam (UNBK) scores in West Nusa Tenggara Province where its UNBK in the lowest position among other provinces in Indonesia. The method used for estimating parameters is weighted least square. The best model is determined based on the Generalized Cross Validation (GCV) minimum value. Based on the estimated 95% confidence interval of parameters of the multi-response truncated spline semiparametric regression model, the results showed that the insignificant factors affecting the UNBK scores were gender and parental education duration while the report card of scores and USBK scores had a positive effect on the UNBK scores but only the UNBK scores of mathematics that report card of scores factor has a negative effect on it.
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Chernozhukov, Victor, Juan Carlos Escanciano, Hidehiko Ichimura, Whitney K. Newey, and James M. Robins. "Locally Robust Semiparametric Estimation." Econometrica 90, no. 4 (2022): 1501–35. http://dx.doi.org/10.3982/ecta16294.

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Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, where first steps have no effect, locally, on average moment functions. Using these orthogonal moments reduces model selection and regularization bias, as is important in many applications, especially for machine learning first steps. Also, associated standard errors are robust to misspecification when there is the same number of moment functions as parameters of interest. We use these orthogonal moments and cross‐fitting to construct debiased machine learning estimators of functions of high dimensional conditional quantiles and of dynamic discrete choice parameters with high dimensional state variables. We show that additional first steps needed for the orthogonal moment functions have no effect, globally, on average orthogonal moment functions. We give a general approach to estimating those additional first steps. We characterize double robustness and give a variety of new doubly robust moment functions. We give general and simple regularity conditions for asymptotic theory.
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Harris, David, and Brendan McCabe. "SEMIPARAMETRIC INDEPENDENCE TESTING FOR TIME SERIES OF COUNTS AND THE ROLE OF THE SUPPORT." Econometric Theory 35, no. 6 (December 26, 2018): 1111–45. http://dx.doi.org/10.1017/s0266466618000403.

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This article considers testing for independence in a time series of small counts within an Integer Autoregressive (INAR) model, taking a semiparametric approach that avoids any distributional assumption on the arrivals process of the model. The nature of the testing problem is shown to differ depending on whether or not the support of the arrivals distribution is the full set of natural numbers (as would be the case for Poisson or Negative Binomial distributions for example) or some strict subset of the natural numbers (such as for a Binomial or Uniform distribution). The theory for these two cases is studied separately.For the case where the arrivals have support on the natural numbers, a new asymptotically efficient semiparametric test, the effective score (Neyman-Rao) test, is derived. The semiparametric Likelihood-Ratio, Wald and score tests are shown to be asymptotically equivalent to the effective score test, and hence also asymptotically efficient. Asymptotic relative efficiency calculations demonstrate that the semiparametric effective score test can provide substantial power advantages over the first order autocorrelation coefficient, which is most commonly applied in practice.For the case where the arrivals have support that is a strict subset of the natural numbers, the theory is considerably altered because the support of the observations becomes different under the null and alternative hypotheses. The semiparametric Likelihood-Ratio, Wald and score tests become asymptotically degenerate in this case, while the effective score test remains valid. Remarkably, in this case the effective score test is also found to have power against local alternatives that shrink to the null at the rate T−1. In rare cases where the arrival support is partly or totally known, additional tests exploiting this information are considered.Finite sample properties of the tests in these various cases demonstrate the semiparametric effective score test can provide substantial power advantages over the first order autocorrelation test implied by a parametric Poisson specification. The simulations also reveal situations in which the first order autocorrelation is preferable in finite samples, so a hybrid of the effective score and autocorrelation tests is proposed to capture most of the benefits of each test.
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Dissertations / Theses on the topic "Semiparametric theory"

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Lee, Sungwook. "Semiparametric regression with random effects /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842547.

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Nishiyama, Yoshihiko. "Higher order asymptotic theory for semiparametric averaged derivatives." Thesis, London School of Economics and Political Science (University of London), 2001. http://etheses.lse.ac.uk/2003/.

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This thesis investigates higher order asymptotic properties of a semiparametric averaged derivative estimator. Classical parametric models assume that we know the distribution function of random variables of interest up to finite dimensional parameters, while nonparametric models do not assume this knowledge. Parametric estimators typically enjoy - consistency and asymptotic normality under certain conditions, while nonparametric estimators converge to the true functionals of interest slower than parametric ones. Semiparametric estimators, a compromise between the two, have been intensively studied since the 1970s. Some of them have been shown to have the same convergence rate as parametric estimators despite involving nonparametric functional estimates. Semiparametric methods often suit econometrics because economic theory typically does not provide the whole information on economic variables which parametric methods require, and a sample of very large size is rarely available in econometrics. This thesis treats a semiparametric averaged derivative estimator of single index models. Its first order asymptotic theory has been studied since late 1980s. It has been shown to be n-consistent and asymptotically normally distributed under certain regularity conditions despite involving a nonparametric density estimate. However its higher order properties could be affected by the property of nonparametric estimates. We obtain valid Edgeworth expansions for both normalized and studentized estimators, and moreover show the bootstrap distribution approximates the exact distribution of the estimator asymptotically as well as the Edgeworth expansion for the normalized statistics. We propose optimal bandwidth choices which minimize the normal approximation error using the expansion. We also examine the finite sample performance of the Edgeworth expansions by a Monte Carlo study.
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Dimitrakopoulos, Stefanos. "Essays on Bayesian semiparametric ordinal-response models." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/66309/.

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Bayesian nonparametric modelling has been widely applied to statistics and econometrics due to the various simulation methods that have been developed and in particular of Markov Chain Monte Carlo (MCMC) techniques. This thesis develops novel Bayesian nonparametric ordinal-response models and proposes efficient MCMC algorithms to estimate them. In chapter 21, we set up a model for inference on panel ordered data and apply it to sovereign credit ratings. In chapter 3, a model for ordinal-valued time series data is considered and is used to examine contagion across stock markets. Using real and simulated data, we show that the proposed models provide a great deal of flexibility in modelling and overcome the standard weakness of Bayesian methods due to the usual parametric assumptions.
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He, Xin. "Semiparametric analysis of panel count data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4774.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on November 27, 2007) Vita. Includes bibliographical references.
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Bouquiaux, Christel. "Semiparametric estimation for extreme values." Doctoral thesis, Universite Libre de Bruxelles, 2005. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210910.

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Nous appliquons la théorie asymptotique des expériences statistiques à des problèmes liés aux valeurs extrêmes. Quatre modèles semi-paramétriques sont envisagés. Tout d'abord le modèle d'échantillonnage de fonction de répartition de type Pareto. L'index de Pareto est le paramètre d'intérêt tandis que la fonction à variation lente, qui intervient dans la décomposition de la fonction de survie, joue le rôle de nuisance. Nous considérons ensuite des observations i.i.d. de fonction de répartition de type Weibull. Le troisième modèle étudié est un modèle de régression. On considère des couples d'observations $(Y_i,X_i)$ indépendants, les v.a. $X_i$ sont i.i.d. de loi connue et on suppose que la fonction de répartition de la loi de $Y$ conditionnellement à $X$ est de type Pareto, avec une fonction à variation lente et un index $gamma$ qui dépendent de $X$. On fait l'hypothèse que la fonction $gamma$ a une forme quelconque mais connue, qui dépend d'un paramètre $\
Doctorat en sciences, Orientation statistique
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Hu, Zonghui. "Semiparametric functional data analysis for longitudinal/clustered data: theory and application." Texas A&M University, 2004. http://hdl.handle.net/1969.1/3088.

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Semiparametric models play important roles in the field of biological statistics. In this dissertation, two types of semiparametic models are to be studied. One is the partially linear model, where the parametric part is a linear function. We are to investigate the two common estimation methods for the partially linear models when the data is correlated — longitudinal or clustered. The other is a semiparametric model where a latent covariate is incorporated in a mixed effects model. We will propose a semiparametric approach for estimation of this model and apply it to the study on colon carcinogenesis. First, we study the profilekernel and backfitting methods in partially linear models for clustered/longitudinal data. For independent data, despite the potential rootn inconsistency of the backfitting estimator noted by Rice (1986), the two estimators have the same asymptotic variance matrix as shown by Opsomer and Ruppert (1999). In this work, theoretical comparisons of the two estimators for multivariate responses are investigated. We show that, for correlated data, backfitting often produces a larger asymptotic variance than the profilekernel method; that is, in addition to its bias problem, the backfitting estimator does not have the same asymptotic efficiency as the profilekernel estimator when data is correlated. Consequently, the common practice of using the backfitting method to compute profilekernel estimates is no longer advised. We illustrate this in detail by following Zeger and Diggle (1994), Lin and Carroll (2001) with a working independence covariance structure for nonparametric estimation and a correlated covariance structure for parametric estimation. Numerical performance of the two estimators is investigated through a simulation study. Their application to an ophthalmology dataset is also described. Next, we study a mixed effects model where the main response and covariate variables are linked through the positions where they are measured. But for technical reasons, they are not measured at the same positions. We propose a semiparametric approach for this misaligned measurements problem and derive the asymptotic properties of the semiparametric estimators under reasonable conditions. An application of the semiparametric method to a colon carcinogenesis study is provided. We find that, as compared with the corn oil supplemented diet, fish oil supplemented diet tends to inhibit the increment of bcl2 (oncogene) gene expression in rats when the amount of DNA damage increases, and thus promotes apoptosis.
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Hu, Huilin. "Large sample theory for pseudo-maximum likelihood estimates in semiparametric models /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/8936.

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Henry, Marc. "Long memory in time series : semiparametric estimation and conditional heteroscedasticity." Thesis, London School of Economics and Political Science (University of London), 1999. http://etheses.lse.ac.uk/1581/.

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This dissertation considers semiparametric spectral estimates of temporal dependence in time series. Semiparametric frequency domain methods rely on a local parametric specification of the spectral density in a neighbourhood of the frequency of interest. Therefore, such methods can be applied to the analysis of singularities in the spectral density at frequency zero to identify long memory. They can also serve as the basis for the estimation of regular parts of the spectrum. One thereby avoids inconsistency that might arise from misspecification of dynamics at frequencies other than the frequency under focus. In case of long financial time series, the loss of efficiency with respect to fully parametric methods (or full band estimates) may be offset by the greater robustness properties. However, if semiparametric frequency domain methods are to be valid tools for inference on financial time series, they need to allow for conditional heteroscedasticity which is now recognized as a dominant feature of asset returns. This thesis provides a general specification which allows the time series under investigation to exhibit this type of behaviour. Two statistics are considered. The weighted periodogram statistic provides asymptotically normal point estimates of the spectral density at zero frequency for weakly dependent processes. The local Whittle (or local frequency domain maximum likelihood) estimate provides asymptotically normal estimates of long memory in possibly strongly dependent processes. The asymptotic results hold irrespective of the behaviour of the spectral density at non zero frequencies. The asymptotic variances are identical to those that obtain under conditional homogeneity in the distribution of the innovations to the observed process. In semiparametric frequency domain estimation, the choice of bandwidth is crucial. Indeed, it determines the asymptotic efficiency of the procedure. Optimal choices of bandwidth are derived, balancing asymptotic bias and asymptotic variance. Feasible versions of these optimal band-widths are proposed, and their performance is assessed in an extensive Monte Carlo study where the innovations to the observed process are simulated under numerous parametric submodels of the general specification, covering a wide range of persistence properties both in the levels and in the squares of the observed process. The techniques described above are applied to the analysis of temporal dependence and persistence in intra-day foreign exchange rate returns and their volatilities. While no strong indication of returns predictability is found in the former, a clear pattern arises in the latter, indicating that intra-day exchange rate returns are well described as martingale differences with weakly stationary and fractionally cointegrated long memory volatilities.
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Lu, Guanhua. "Asymptotic theory for multiple-sample semiparametric density ratio models and its application to mortality forecasting." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7615.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.
Thesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Van, Bever Germain. "Contributions to nonparametric and semiparametric inference based on statistical depth." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209438.

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L'objectif général de cette thèse est d'introduire de nouveaux concepts ou d'étendre certaines procédures statistiques déjà existantes touchant à la notion de profondeur statistique.

Celle-ci, originellement introduite afin de généraliser la notion de médiane et de fournir naturellement un ordre (depuis un centre, vers l'extérieur) dans un contexte multivarié, a, depuis son développement, démontré ses nombreuses qualités, tant en termes de robustesse, que d'utilité dans de nombreuses procédures inférentielles.

Les résultats proposés dans ce travail se développent le long de trois axes.

Pour commencer, la thèse s'intéresse à la classification supervisée. La profondeur a, en effet, déjà été utilisée avec succès dans ce contexte. Cependant, jusqu'ici, les outils développés restaient limités aux distributions elliptiques, constituant ainsi une sévère restriction des méthodes utilisant les fonctions de profondeur, qui, pour la plupart, sont par essence nonparamétrique. La première partie de cette thèse propose donc une nouvelle méthode de classification, fondée sur la profondeur, dont on montrera qu'elle est essentiellement universellement convergente. En particulier, la règle de discrimination proposée se fonde sur les idées utilisées dans la classification par plus proches voisins, en introduisant cependant des voisinages fondés sur la profondeur, mieux à même de cerner le comportement des populations sous-jacentes.

Ces voisinages d'un point quelconque, et surtout l'information sur le comportement local de la distribution en ce point qu'ils apportent, ont été réutilisés dans la seconde partie de ce travail. Plusieurs auteurs ont en effet reconnu certaines limitations aux fonctions de profondeur, de par leur caractère global et la difficulté d'étudier par leur biais des distributions multimodales ou à support convexe. Une nouvelle définition de profondeur locale est donc développée et étudiée. Son utilité dans différents problèmes d'inférence est également explorée.

Enfin, la thèse s'intéresse au paramètre de forme pour les distributions elliptiques. Ce paramètre d'importance est utilisé dans de nombreuses procédures statistiques (analyse en composantes principales, analyse en corrélations canoniques, entre autres) et aucune fonction de profondeur pour celui-ci n'existait à ce jour. La profondeur de forme est donc définie et ses propriétés sont étudiées. En particulier, on montrera que le cadre général de la profondeur paramétrique n'est pas suffisant en raison de la présence du paramètre de nuisance (d'influence non nulle) qu'est l'échelle. Une application inférentielle est présentée dans le cadre des tests d'hypothèses.
Doctorat en Sciences
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Books on the topic "Semiparametric theory"

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Horowitz, Joel L. Semiparametric methods in econometrics. New York: Springer, 1998.

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Horowitz, Joel. Semiparametric methods in econometrics. New York: Springer, 1998.

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M. J. van der Laan. Efficient and inefficient estimation in semiparametric models. Amsterdam, Netherlands: Centrum voor Wiskunde en Informatica, 1995.

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Dipak, Dey, Müller, Peter, 1963 Aug. 9-, and Sinha Debajyoti, eds. Practical nonparametric and semiparametric Bayesian statistics. New York: Springer, 1998.

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J, Bickel Peter, ed. Efficient and adaptive estimation for semiparametric models. New York: Springer, 1998.

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Crépon, Bruno. Testing exclusion restrictions at infinity in the semiparametric selection model. Bonn, Germany: IZA, 2006.

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Lee, Myoung-jae. Methods of moments and semiparametric econometrics for limited dependent and variable models. New York: Springer, 1996.

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Racine, J. S. The semiparametric approach to the estimation of systems of equations models in the presence of heteroskedasticity of unknown form. Toronto, Ont: Dept. of Economics, York University, 1989.

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International Symposium in Economic Theory and Econometrics (5th 1988 Duke University). Nonparametric and semiparametric methods in econometrics and statistics: Proceedings of the Fifth International Symposium in Economic Theory and Econometrics. Cambridge [England]: Cambridge University Press, 1991.

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Semiparametric Theory and Missing Data. New York, NY: Springer New York, 2006. http://dx.doi.org/10.1007/0-387-37345-4.

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Book chapters on the topic "Semiparametric theory"

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Duchesne, Thierry. "Semiparametric Methods of Time Scale Selection." In Recent Advances in Reliability Theory, 279–90. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1384-0_18.

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Bagdonavičius, Vilijandas, and Mikhail Nikulin. "Semiparametric Estimation in Accelerated Life Testing." In Recent Advances in Reliability Theory, 405–18. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1384-0_26.

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Ozeki, Akichika, and Kjell Doksum. "An Analysis of Extremes: Semiparametric Efficiency in Regression." In Pioneering Works on Extreme Value Theory, 71–91. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0768-4_4.

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Kennedy, Edward H. "Semiparametric Theory and Empirical Processes in Causal Inference." In Statistical Causal Inferences and Their Applications in Public Health Research, 141–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41259-7_8.

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Erdely, Arturo, and Martin Diaz-Viera. "Nonparametric and Semiparametric Bivariate Modeling of Petrophysical Porosity-Permeability Dependence from Well Log Data." In Copula Theory and Its Applications, 267–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12465-5_13.

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Yang, Song. "Semiparametric Analysis of Treatment Effect via Failure Probability Ratio and the Ratio of Cumulative Hazards." In Contemporary Developments in Statistical Theory, 329–51. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02651-0_21.

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Mouchart, Michel, Jean-Marie Rolin, and Eliana Scheihing. "Identification of Semiparametric Binary Response Models: Sampling Theory and Bayesian Approaches Compared." In Nonparametric Bayesian Inference, 341–63. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61329-6_14.

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Su, Liangjun, Aman Ullah, Santosh Mishra, and Yun Wang. "Nonparametric and Semiparametric Volatility Models: Specification, Estimation, and Testing." In Handbook of Volatility Models and Their Applications, 269–91. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118272039.ch11.

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Lai, Tze Leung, and Zheng Su. "Sequential nonparametrics and semiparametrics: Theory, implementation and applications to clinical trials." In Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen, 332–49. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2008. http://dx.doi.org/10.1214/193940307000000257.

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"BAYES IAN INFERENCE IN SEMIPARAMETRIC PROBLEMS." In Mathematical Statistics Theory and Applications, 27–30. De Gruyter, 1987. http://dx.doi.org/10.1515/9783112319086-003.

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Conference papers on the topic "Semiparametric theory"

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Egger, Bernhard, Dinu Kaufmann, Sandro Schönborn, Volker Roth, and Thomas Vetter. "Copula Eigenfaces - Semiparametric Principal Component Analysis for Facial Appearance Modeling." In International Conference on Computer Graphics Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005718800480056.

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Sommeregger, Lukas, and Horst Lewitschnig. "A Semiparametric Transition Model for Lifetime Drift of Discrete Electrical Parameters in Semiconductor Devices." In 4th International Conference on Statistics: Theory and Applications (ICSTA'22). Avestia Publishing, 2022. http://dx.doi.org/10.11159/icsta22.133.

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SOME, Sobom M., and Célestin C. KOKONENDJI. "Flexible Semiparametric Kernel Estimation with Bayesian Local Bandwidths and Diagnostics for Multivariate Count Data." In 4th International Conference on Statistics: Theory and Applications (ICSTA'22). Avestia Publishing, 2022. http://dx.doi.org/10.11159/icsta22.126.

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Balekelayi, Ngandu, and Solomon Tesfamariam. "Time Dependent Reliability Analysis for Oil and Gas Pipelines: A Bayesian Spectral Analysis-Based Deterioration Model." In 2020 13th International Pipeline Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/ipc2020-9284.

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Abstract Oil and gas pipelines are essential infrastructures that sustain the economy of modern society. They are designed for continuous and reliable operations over their service lives. Once installed, however, their reliability is affected by several threats among which external corrosion plays a significant role. Corrosion-based pit depth growth reduces the wall thickness over time that consequently affect the mechanical strength and the hydraulic performance of the pipeline. Pipeline utility managers rely on the corrosion growth rate models to plan their maintenance, rehabilitation and/or replacement. Existing pipeline deterioration models are mostly based on the power law function that relates the pit depth with the exposure time and rarely include the soil factors that can have effect on the corrosion growth rate. Moreover, the way these factors affect the corrosion rate is complex and cannot be captured with simple linear relationship. This paper uses data found in the literature to build a nonlinear pit depth growth model based on Bayesian spectral analysis regression technique. All continuous covariates are allowed to have smooth nonlinear spectral representations of their effect function on the pit depth growth. The discrete (i.e. categorical) factors are modeled using the ordinary least squared algorithm. The final semiparametric model allows to capture all pit depth measurements, even those difficult to be modeled using high degree polynomials. The stochastic nature of the pit depth growth is captured through the Bayesian approach. A time dependent reliability analysis using subset simulation is carried out to evaluate the changes occurring in the probability of failure of the pipe over time and allow for a better planning and management of these important infrastructure. The model is applied on a bare pipe directly exposed to the soil environment over time. The Bayesian pit depth growth model is accurate enough to allow the computation of the time dependent reliability of pipelines considering both the mechanical and hydraulic reliabilities.
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Reports on the topic "Semiparametric theory"

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Angrist, Joshua, Òscar Jordà, and Guido Kuersteiner. Semiparametric Estimates of Monetary Policy Effects: String Theory Revisited. Cambridge, MA: National Bureau of Economic Research, August 2013. http://dx.doi.org/10.3386/w19355.

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Volpe Martincus, Christian, and Jerónimo Carballo. Beyond The Average Effects: The Distributional Impacts of Export Promotion Programs in Developing Countries. Inter-American Development Bank, August 2010. http://dx.doi.org/10.18235/0011214.

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Do all exporters benefit the same from export promotion programs? Surprisingly, not matter how obvious this question may a priori be when thinking of the effectiveness of these programs there is virtually no empirical evidence on how they affect export performance in different parts of the distribution of export outcomes. This paper aims at filling this gap in the literature. We assess the distributional impacts of trade promotion activities performing efficient semiparametric quantile treatment effect estimation on assistance, total sales, and highly disaggregated export data for the whole population of Chilean exporters over the period 2002-2006. We find that these activities have indeed heterogeneous effects over the distribution of export performance, along both the extensive and intensive margins. In particular, smaller firms as measured by their total exports seem to benefit more from export promotion actions.
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Arango-Castillo, Lenin, Francisco J. Martínez-Ramírez, and María José Orraca. Univariate Measures of Persistence: A Comparative Analysis. Banco de México, September 2024. http://dx.doi.org/10.36095/banxico/di.2024.11.

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Persistence is the speed with which a time series returns to its mean after a shock. Although several measures of persistence have been proposed in the literature, when they are empirically applied, the different measures indicate incompatible messages, as they differ both in the level and the implied evolution of persistence. One plausible reason why persistence estimators may differ is the presence of data particularities such as trends, cycles, measurement errors, additive and temporary change outliers, and structural changes. To gauge the usefulness and robustness of different measures of persistence, we compare them in a univariate time series framework using Monte Carlo simulations. We consider nonparametric, semiparametric, and parametric time-domain and frequency-domain persistence estimators and investigate their performance under different anomalies found in practice. Our results indicate that the nonparametric method is, on average, less affected by the different types of time series anomalies analyzed in this work.
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