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Relevant bibliographies by topics / Non-parametric and semiparametric model

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Academic literature on the topic 'Non-parametric and semiparametric model'

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Published: 4 May 2024

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Journal articles on the topic "Non-parametric and semiparametric model"

1

Huang, Mian, Qinghua Ji, and Weixin Yao. "Semiparametric hidden Markov model with non-parametric regression." Communications in Statistics - Theory and Methods 47, no. 21 (November 20, 2017): 5196–204. http://dx.doi.org/10.1080/03610926.2017.1388398.

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Prasetyo, Hanung, Ferra Arik Tridalestari, and Wawa Wikusna. "IMPLEMENTATION OF SURVIVAL ESTIMATION OF BONE MARROW TRANSPLANT PATIENTS WITH SEMIPARAMETRIC HAZARD FUNCTION USING MINITAB SOFTWARE." Jurnal Teknik Informatika (Jutif) 3, no. 5 (October 24, 2022): 1177–82. http://dx.doi.org/10.20884/1.jutif.2022.3.5.280.

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The Hazard Rate probability value estimation method is an estimation model that is carried out fully parametrically or it can also be done by non-parametric methods. Sometimes using the parametric method will give biased value results because it gives too much value in general, while the non-parametric estimation method causes the variance value to be too high. Therefore, for some cases there is a way to combine the two methods, which is called the Semiparametric method, which is an estimation method that has the characteristics of improving non-parametric parametric estimates. This paper shows that the semiparametric hazard method gives better results than parametric and non-parametric methods. The basis for developing the semiparametric probability method is to roughly estimate the probability of a parametric conjecture as a first step and then proceed with several correction models for setting data. The implementation of the probability value in this study uses the Life Time data of Transplant bone patients at Hospital X with the help of Minitab software analysis.

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Boubeta, Miguel, María José Lombardía, Wenceslao González-Manteiga, and Manuel Francisco Marey-Pérez. "Burned area prediction with semiparametric models." International Journal of Wildland Fire 25, no. 6 (2016): 669. http://dx.doi.org/10.1071/wf15125.

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Wildfires are one of the main causes of forest destruction, especially in Galicia (north-west Spain), where the area burned by forest fires in spring and summer is quite high. This work uses two semiparametric time-series models to describe and predict the weekly burned area in a year: autoregressive moving average (ARMA) modelling after smoothing, and smoothing after ARMA modelling. These models can be described as a sum of a parametric component modelled by an autoregressive moving average process and a non-parametric one. To estimate the non-parametric component, local linear and kernel regression, B-splines and P-splines were considered. The methodology and software were applied to a real dataset of burned area in Galicia for the period 1999–2008. The burned area in Galicia increases strongly during summer periods. Forest managers are interested in predicting the burned area to manage resources more efficiently. The two semiparametric models are analysed and compared with a purely parametric model. In terms of error, the most successful results are provided by the first semiparametric time-series model.

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Side, Syafruddin, Wahidah Sanusi, and Mustati'atul Waidah Maksum. "Model Regresi Semiparametrik Spline untuk D ata Longitudinal pada Kasus Demam Berdarah Dengue di Kota Makassar." Journal of Mathematics Computations and Statistics 3, no. 1 (February 12, 2021): 20. http://dx.doi.org/10.35580/jmathcos.v3i1.19181.

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Abstrak. Regresi semiparametrik merupakan model regresi yang memuat komponen parametrik dan komponen nonparametrik dalam suatu model. Pada penelitian ini digunakan model regresi semiparametrik spline untuk data longitudinal dengan studi kasus penderita Demam Berdarah Dengue (DBD) di Rumah Sakit Universitas Hasanuddin Makassar periode bulan Januari sampai bulan Maret 2018. Estimasi model regresi terbaik didapat dari pemilihan titik knot optimal dengan melihat nilai Generalized Cross Validation (GCV) dan Mean Square Error (MSE) yang minimum. Komponen parametrik pada penelitian ini adalah hemoglobin (g/dL) dan umur (tahun), suhu tubuh ( ), trombosit ( ) sebagai komponen nonparametrik dengan nilai GCV minimum sebesar 221,67745153 dicapai pada titik knot yaitu 14,552; 14,987; dan 15,096; nilai MSE sebesar 199,1032; dan nilai koefisien determinasi sebesar 75,3% yang diperoleh dari model regresi semiparametrik spline linear dengan tiga titik knot..Kata Kunci: regresi semiparametrik, spline, knot, Generalized Cross Validation, Demam Berdarah Dengue.Abstract. Semiparametric regression is a regression model that includes parametric and nonparametric components in it. The regression model in this research is spline semiparametric regression with case studies of patients with Dengue Hemorrahagic Fever (DHF) at University of Hasanuddin Makassar Hospital during the period of January to March 2018. The best regression model estimation is obtained from the selection of optimal knot which has minimum Generalized Cross Validation (GCV) and Mean Square Error (MSE). Parametric component in this research is hemoglobin (g/dL) and age (years), body temperature ( ), platelets ( ) as a nonparametric components. The minimum value of GCV is 221,67745153 achieved at the point 14,552; 14,987; and 15,096 knot; MSE value of 199,1032; and the value of coefficient determination is 75,3% obtained from semiparametric regression model linear spline with third point of knots.Keywords: semiparametric regression, spline, knot, Generalized Cross Validation, Dengue Hemorrahagic Fever.

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Abd El-Monsef, Mohamed, Elhoussainy Rady, and Ayat Sobhy. "WEIBULL SEMIPARAMETRIC REGRESSION MODELS UNDER RANDOM CENSORSHIP." JOURNAL OF ADVANCES IN MATHEMATICS 11, no. 8 (December 22, 2015): 5577–82. http://dx.doi.org/10.24297/jam.v11i8.1209.

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Semiparametric regression is concerned with the flexible combination of non-linear functional relationships in regression analysis. The main advantage of the semiparametric regression models is that any application benefits from regression analysis can also benefit from the semiparametric regression. In this paper, we derived a consistent estimator of parametric portion and nonparametric portion in Weibull semi-parametric regression models under random censorship.

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Horowitz, Joel L., and Wolfgang Härdle. "Testing a Parametric Model Against a Semiparametric Alternative." Econometric Theory 10, no. 5 (December 1994): 821–48. http://dx.doi.org/10.1017/s0266466600008872.

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This paper describes a method for testing a parametric model of the mean of a random variable Y conditional on a vector of explanatory variables X against a semiparametric alternative. The test is motivated by a conditional moment test against a parametric alternative and amounts to replacing the parametric alternative model with a semiparametric model. The resulting semiparametric test is consistent against a larger set of alternatives than are parametric conditional moments tests based on finitely many moment conditions. The results of Monte Carlo experiments and an application illustrate the usefulness of the new test.

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Yogatama, Dani, Cyprien de Masson d’Autume, and Lingpeng Kong. "Adaptive Semiparametric Language Models." Transactions of the Association for Computational Linguistics 9 (2021): 362–73. http://dx.doi.org/10.1162/tacl_a_00371.

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Abstract We present a language model that combines a large parametric neural network (i.e., a transformer) with a non-parametric episodic memory component in an integrated architecture. Our model uses extended short-term context by caching local hidden states—similar to transformer-XL—and global long-term memory by retrieving a set of nearest neighbor tokens at each timestep. We design a gating function to adaptively combine multiple information sources to make a prediction. This mechanism allows the model to use either local context, short-term memory, or long-term memory (or any combination of them) on an ad hoc basis depending on the context. Experiments on word-based and character-based language modeling datasets demonstrate the efficacy of our proposed method compared to strong baselines.

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Rizki, Akbar, and Abdul Aziz Nurussadad. "PEMODELAN SEMIPARAMETRIK STATISTICAL DOWNSCALING UNTUK MENDUGA CURAH HUJAN BULANAN DI INDRAMAYU." Xplore: Journal of Statistics 2, no. 2 (August 31, 2018): 1–6. http://dx.doi.org/10.29244/xplore.v2i2.117.

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Semiparametric statistical downscaling (SD) model is a statistical model which consists of parametric and non-parametric functional relationship between local scale and global scale variable. This study used rainfall intensity in Indramayu as local scale variable and Global Precipitation Climatology Project (GPCP) precipitation as global scale variable. GPCP precipitation data have multicollinearity, therefore they were reduced by principal component analysis. Eight principal components which have been selected then used as the prediktors and rainfall intensity in Indramayu as the response. Semiparametric SD model was used to predict the rainfall intensity in the district of Indramayu. The semiparametric model developed by mixed model approach where the nonparametric relationship is represented using spline with truncated power basis. Linier semiparametric model is the best model to estimate monthly rainfall in indramayu district. The model performance evaluated by RMSEP (root mean square error prediction) and (coefficient of determination). The result shows that the best model have values of RMSEP and are 61.64 and 71%.

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Song, Yunquan, Yaqi Liu, and Hang Su. "Robust Variable Selection for Single-Index Varying-Coefficient Model with Missing Data in Covariates." Mathematics 10, no. 12 (June 10, 2022): 2003. http://dx.doi.org/10.3390/math10122003.

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As applied sciences grow by leaps and bounds, semiparametric regression analyses have broad applications in various fields, such as engineering, finance, medicine, and public health. Single-index varying-coefficient model is a common class of semiparametric models due to its flexibility and ease of interpretation. The standard single-index varying-coefficient regression models consist mainly of parametric regression and semiparametric regression, which assume that all covariates can be observed. The assumptions are relaxed by taking the models with missing covariates into consideration. To eliminate the possibility of bias due to missing data, we propose a probability weighted objective function. In this paper, we investigate the robust variable selection for a single-index varying-coefficient model with missing covariates. Using parametric and nonparametric estimates of the likelihood of observations with fully observed covariates, we examine the estimators for estimating the likelihood of observations. For variable selection, we use a weighted objective function penalized by a non-convex SCAD. Theoretical challenges include the treatment of missing data and a single-index varying-coefficient model that uses both the non-smooth loss function and the non-convex penalty function. We provide Monte Carlo simulations to evaluate the performance of our approach.

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De-Graft Acquah, Henry, and Lawrence Acheampong. "Comparing parametric and semiparametric error correction models for estimation of long run equilibrium between exports and imports." Applied Studies in Agribusiness and Commerce 11, no. 1-2 (June 30, 2017): 19–23. http://dx.doi.org/10.19041/apstract/2017/1-2/3.

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This paper introduces the semiparametric error correction model for estimation of export-import relationship as an alternative to the least squares approach. The intent is to demonstrate how semiparametric error correction model can be used to estimate the relationship between Ghana’s export and import within the context of a generalized additive modelling (GAM) framework. The semiparametric results are compared to common parametric specification using the ordinary least squares regression. The results from the semiparametric and parametric error correction models (ECM) indicate that the error correction term and import variable are significant determinants of Ghana’s exports. On the basis of Akaike Information Criteria and Generalized Cross-Validation (GCV) scores, it is found that the semiparametric error correction model provides a better fit than the widely used parametric error correction model for modeling Ghana’s export-import relationship. The results of the analysis of variance provide further evidence of nonlinearity in Ghana’s export and import relationship. In effect, this paper demonstrates the usefulness of semiparametric error correction model in the estimation of export – import relationship. JEL code: C14, C18, C22, F10, F14

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Dissertations / Theses on the topic "Non-parametric and semiparametric model"

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Yan, Boping. "Double kernel non-parametric estimation in semiparametric econometric models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ35817.pdf.

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Mays, James Edward. "Model robust regression: combining parametric, nonparametric, and semiparametric methods." Diss., Virginia Polytechnic Institute and State University, 1995. http://hdl.handle.net/10919/49937.

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In obtaining a regression fit to a set of data, ordinary least squares regression depends directly on the parametric model formulated by the researcher. If this model is incorrect, a least squares analysis may be misleading. Alternatively, nonparametric regression (kernel or local polynomial regression, for example) has no dependence on an underlying parametric model, but instead depends entirely on the distances between regressor coordinates and the prediction point of interest. This procedure avoids the necessity of a reliable model, but in using no information from the researcher, may fit to irregular patterns in the data. The proper combination of these two regression procedures can overcome their respective problems. Considered is the situation where the researcher has an idea of which model should explain the behavior of the data, but this model is not adequate throughout the entire range of the data. An extension of partial linear regression and two methods of model robust regression are developed and compared in this context. These methods involve parametric fits to the data and nonparametric fits to either the data or residuals. The two fits are then combined in the most efficient proportions via a mixing parameter. Performance is based on bias and variance considerations.
Ph. D.
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Zhang, Tianyang. "Partly parametric generalized additive model." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/913.

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In many scientific studies, the response variable bears a generalized nonlinear regression relationship with a certain covariate of interest, which may, however, be confounded by other covariates with unknown functional form. We propose a new class of models, the partly parametric generalized additive model (PPGAM) for doing generalized nonlinear regression with the confounding covariate effects adjusted nonparametrically. To avoid the curse of dimensionality, the PPGAM specifies that, conditional on the covariates, the response distribution belongs to the exponential family with the mean linked to an additive predictor comprising a nonlinear parametric function that is of main interest, plus additive, smooth functions of other covariates. The PPGAM extends both the generalized additive model (GAM) and the generalized nonlinear regression model. We propose to estimate a PPGAM by the method of penalized likelihood. We derive some asymptotic properties of the penalized likelihood estimator, including consistency and asymptotic normality of the parametric estimator of the nonlinear regression component. We propose a model selection criterion for the PPGAM, which resembles the BIC. We illustrate the new methodologies by simulations and real applications. We have developed an R package PPGAM that implements the methodologies expounded herein.

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Song, Rui, Shikai Luo, Donglin Zeng, Hao Helen Zhang, Wenbin Lu, and Zhiguo Li. "Semiparametric single-index model for estimating optimal individualized treatment strategy." INST MATHEMATICAL STATISTICS, 2017. http://hdl.handle.net/10150/625783.

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Different from the standard treatment discovery framework which is used for finding single treatments for a homogenous group of patients, personalized medicine involves finding therapies that are tailored to each individual in a heterogeneous group. In this paper, we propose a new semiparametric additive single-index model for estimating individualized treatment strategy. The model assumes a flexible and nonparametric link function for the interaction between treatment and predictive covariates. We estimate the rule via monotone B-splines and establish the asymptotic properties of the estimators. Both simulations and an real data application demonstrate that the proposed method has a competitive performance.

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Starnes, Brett Alden. "Asymptotic Results for Model Robust Regression." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/30244.

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Since the mid 1980's many statisticians have studied methods for combining parametric and nonparametric esimates to improve the quality of fits in a regression problem. Notably in 1987, Einsporn and Birch proposed the Model Robust Regression estimate (MRR1) in which estimates of the parametric function, f, and the nonparametric function, g, were combined in a straightforward fashion via the use of a mixing parameter, l. This technique was studied extensively at small samples and was shown to be quite effective at modeling various unusual functions. In 1995, Mays and Birch developed the MRR2 estimate as an alternative to MRR1. This model involved first forming the parametric fit to the data, and then adding in an estimate of g according to the lack of fit demonstrated by the error terms. Using small samples, they illustrated the superiority of MRR2 to MRR1 in most situations. In this dissertation we have developed asymptotic convergence rates for both MRR1 and MRR2 in OLS and GLS (maximum likelihood) settings. In many of these settings, it is demonstrated that the user of MRR1 or MRR2 achieves the best convergence rates available regardless of whether or not the model is properly specified. This is the "Golden Result of Model Robust Regression". It turns out that the selection of the mixing parameter is paramount in determining whether or not this result is attained.
Ph. D.

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Abdel-Salam, Abdel-Salam Gomaa. "Profile Monitoring with Fixed and Random Effects using Nonparametric and Semiparametric Methods." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/29387.

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Profile monitoring is a relatively new approach in quality control best used where the process data follow a profile (or curve) at each time period. The essential idea for profile monitoring is to model the profile via some parametric, nonparametric, and semiparametric methods and then monitor the fitted profiles or the estimated random effects over time to determine if there have been changes in the profiles. The majority of previous studies in profile monitoring focused on the parametric modeling of either linear or nonlinear profiles, with both fixed and random effects, under the assumption of correct model specification. Our work considers those cases where the parametric model for the family of profiles is unknown or at least uncertain. Consequently, we consider monitoring profiles via two techniques, a nonparametric technique and a semiparametric procedure that combines both parametric and nonparametric profile fits, a procedure we refer to as model robust profile monitoring (MRPM). Also, we incorporate a mixed model approach to both the parametric and nonparametric model fits. For the mixed effects models, the MMRPM method is an extension of the MRPM method which incorporates a mixed model approach to both parametric and nonparametric model fits to account for the correlation within profiles and to deal with the collection of profiles as a random sample from a common population. For each case, we formulated two Hotelling's T 2 statistics, one based on the estimated random effects and one based on the fitted values, and obtained the corresponding control limits. In addition,we used two different formulas for the estimated variancecovariance matrix: one based on the pooled sample variance-covariance matrix estimator and a second one based on the estimated variance-covariance matrix based on successive differences. A Monte Carlo study was performed to compare the integrated mean square errors (IMSE) and the probability of signal of the parametric, nonparametric, and semiparametric approaches. Both correlated and uncorrelated errors structure scenarios were evaluated for varying amounts of model misspecification, number of profiles, number of observations per profile, shift location, and in- and out-of-control situations. The semiparametric (MMRPM) method for uncorrelated and correlated scenarios was competitive and, often, clearly superior with the parametric and nonparametric over all levels of misspecification. For a correctly specified model, the IMSE and the simulated probability of signal for the parametric and theMMRPM methods were identical (or nearly so). For the severe modelmisspecification case, the nonparametric andMMRPM methods were identical (or nearly so). For the mild model misspecification case, the MMRPM method was superior to the parametric and nonparametric methods. Therefore, this simulation supports the claim that the MMRPM method is robust to model misspecification. In addition, the MMRPM method performed better for data sets with correlated error structure. Also, the performances of the nonparametric and MMRPM methods improved as the number of observations per profile increases since more observations over the same range of X generally enables more knots to be used by the penalized spline method, resulting in greater flexibility and improved fits in the nonparametric curves and consequently, the semiparametric curves. The parametric, nonparametric and semiparametric approaches were utilized for fitting the relationship between torque produced by an engine and engine speed in the automotive industry. Then, we used a Hotelling's T 2 statistic based on the estimated random effects to conduct Phase I studies to determine the outlying profiles. The parametric, nonparametric and seminonparametric methods showed that the process was stable. Despite the fact that all three methods reach the same conclusion regarding the –in-control– status of each profile, the nonparametric and MMRPM results provide a better description of the actual behavior of each profile. Thus, the nonparametric and MMRPM methods give the user greater ability to properly interpret the true relationship between engine speed and torque for this type of engine and an increased likelihood of detecting unusual engines in future production. Finally, we conclude that the nonparametric and semiparametric approaches performed better than the parametric approach when the user's model is misspecified. The case study demonstrates that, the proposed nonparametric and semiparametric methods are shown to be more efficient, flexible and robust to model misspecification for Phase I profile monitoring in a practical application. Thus, our methods are robust to the common problem of model misspecification. We also found that both the nonparametric and the semiparametric methods result in charts with good abilities to detect changes in Phase I data, and in charts with easily calculated control limits. The proposed methods provide greater flexibility and efficiency than current parametric methods used in profile monitoring for Phase I that rely on correct model specification, an unrealistic situation in many practical problems in industrial applications.
Ph. D.

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Margevicius, Seunghee P. "Modeling of High-Dimensional Clinical Longitudinal Oxygenation Data from Retinopathy of Prematurity." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1523022165691473.

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Bijou, Mohammed. "Qualité de l'éducation, taille des classes et mixité sociale : Un réexamen à partir des méthodes à variables instrumentales et semi-paramétriques sur données multiniveaux - Cas du Maroc -." Electronic Thesis or Diss., Toulon, 2021. http://www.theses.fr/2021TOUL2004.

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L’objectif de ce travail est d’évaluer la qualité du système éducatif marocain à partir des données du programme TIMSS et PIRLS 2011. Le travail s’articule autour de trois chapitres. Il s’agit d’étudier, dans le premier chapitre, l’influence des caractéristiques individuelles de l’élève et de l’école sur les performances scolaires, ainsi que le rôle important de l'environnement scolaire (effet taille et composition sociale). Dans le deuxième chapitre, nous cherchons à estimer la taille de classe optimale qui assure une réussite généralisée de tous les élèves des deux niveaux à savoir, la 4e année primaire et la 2e année collégiale. Le troisième chapitre propose d’étudier la relation existante entre la composition sociale et économique de l’établissement et la performance scolaire, tout en démontrant le rôle de la mixité sociale dans la réussite des élèves. Pour ce faire, nous avons utilisé différentes approches économétriques, à savoir une modélisation multiniveau avec correction du problème de l’endogénéité (chapitre 1), un modèle semi-paramétrique hiérarchique dans le (chapitre 2) et un modèle semi paramétrique hiérarchique contextuel (chapitre 3). Les résultats montrent que la performance scolaire est déterminée par plusieurs facteurs intrinsèques à l'élève et également contextuels. En effet, une taille de classe moins chargée et une école à composition sociale mixte sont les deux éléments essentiels pour un environnement favorable et un apprentissage assuré pour l’ensemble des élèves. Selon nos résultats, les pouvoirs publics devraient accorder la priorité à la réduction de la taille des classes en la limitant à 27 élèves au maximum. De plus, il est nécessaire d’envisager un assouplissement de la carte scolaire afin de favoriser la mixité sociale à l’école. Les résultats obtenus permettent une meilleure compréhension du système scolaire marocain, dans son aspect qualitatif et la justification des politiques éducatives pertinentes pour améliorer la qualité du système éducatif marocain
This thesis objective is to examine the quality of the Moroccan education system exploiting the data of the programs TIMSS and PIRLS 2011.The thesis is structured around three chapters. The first chapter examines the influence of individual student and school characteristics on school performance, as well as the important role of the school environment (effect of size and social composition). In the second chapter, we seek to estimate the optimal class size that ensures widespread success for all students at both levels, namely, the fourth year of primary school and the second year of college. The third chapter proposes to study the relationship between the social and economic composition of the school and academic performance, while demonstrating the role of social mix in student success. In order to study this relationship, we mobilize different econometric approaches, by applying a multilevel model with correction for the problem of endogeneity (chapter 1), a hierarchical semi-parametric model (chapter 2) and a contextual hierarchical semi-parametric model (chapter 3). The results show that academic performance is determined by several factors that are intrinsic to the student and also contextual. Indeed, a smaller class size and a school with a mixed social composition are the two essential elements for a favourable environment and assured learning for all students. According to our results, governments should give priority to reducing class size by limiting it to a maximum of 27 students. In addition, it is necessary to consider making the school map more flexible in order to promote social mixing at school. The results obtained allow a better understanding of the Moroccan school system, in its qualitative aspect and the justification of relevant educational policies to improve the quality of the Moroccan education system

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Avramidis, Panagiotis. "Estimation of the volatility function : non-parametric and semiparametric approaches." Thesis, London School of Economics and Political Science (University of London), 2004. http://etheses.lse.ac.uk/1793/.

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We investigate two problems in modelling time series data that exhibit conditional heteroscedasticity. The first part deals with the local maximum likelihood estimation of volatility functions which are in the form of conditional variance functions. The existing estimation procedures yield plausible results. Yet, they often fail to take into account special features of the data at the cost of reduced accuracy of prediction. More precisely, many of the parametric and nonparametric conditional variance models ignore the fact that the error distribution departs significantly from gaussian distribution. We propose a novel nonparametric estimation procedure that replaces popular local least squares method with local maximum likelihood estimation. Intuitively, using information from the error distribution improves the estimators and therefore increases the accuracy in prediction. This conclusion is proved theoretically and illustrated by numerical examples. In addition, we show that the proposed estimator adapts asymptotically to the error distribution as well as to the mean regression function. Applications with real data examples demonstrate the potential use of the adaptive maximum likelihood estimator in financial risk management. The second part deals with the variable selection for a particular class of semipara-metric models known as the partial linear models. The existing selection methods are computationally demanding. The proposed selection procedure is computationally more efficient. In particular, if P and Q are the number of linear and nonparametric candidate regressors, respectively, then the proposed procedure reduces the order of the number of variable subsets to be investigated from 2 Q+P to 2Q + 2 P. At the same time, it maintains all the good properties of existing methods, such as consistency. The latter is proven theoretically and confirmed numerically by simulated examples. The results are presented for the mean regression function while the generalization to the conditional variance function is discussed separately.

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Tachet, des combes Rémi. "Non-parametric model calibration in finance." Phd thesis, Ecole Centrale Paris, 2011. http://tel.archives-ouvertes.fr/tel-00658766.

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Consistently fitting vanilla option surfaces is an important issue when it comes to modelling in finance. In three different models: local and stochastic volatility, local correlation and hybrid local volatility with stochstic rates, this calibration boils down to the resolution of a nonlinear partial integro-differential equation. In a first part, we give existence results of solutions for the calibration equation. They are based upon fixed point methods in Hölder spaces and short-time a priori estimates. We then apply those existence results to the three models previously mentioned and give the calibration obtained when solving the pde numerically. At last, we focus on the algorithm used for the resolution: an ADI predictor/corrector scheme that needs to be modified to take into account the nonlinear term. We also study an instability phenomenon that occurs in certain cases for the local and stochastic volatility model. Using Hadamard's theory, we try to offer a theoretical explanation to the instability

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Books on the topic "Non-parametric and semiparametric model"

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Schafgans, Marcia M. A. Gender wage differences in Malaysia: Parametric and semiparametric estimation. London: Suntory and Toyota International Centres for Economics and Related Disciplines, 1997.

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Aman, Ullah, ed. Semiparametric and nonparametric econometrics. Heidelberg: Physica-Verlag, 1989.

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Balakrishnan, N., M. S. Nikulin, M. Mesbah, and N. Limnios, eds. Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-0-8176-8206-4.

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Knight, John L. Pricing interest rate derivatives in a non-parametric two-factor term-structure model. [Ottawa]: Bank of Canada, 1999.

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Wood, Douglas. The United Kingdom overnight interbank market: A non-parametric nonlinear directional tracking model. Manchester: Manchester Business School, 1995.

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Brandt, James M. A parametric cost model for estimating operating and support costs of US Navy (Non-Nuclear) surface ships. Monterey, Calif: Naval Postgraduate School, 1999.

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Semiparametric Odds Ratio Model and Its Applications. Taylor & Francis Group, 2021.

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Chen, Hua Yun. Semiparametric Odds Ratio Model and Its Applications. CRC Press LLC, 2021.

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Chen, Hua Yun. Semiparametric Odds Ratio Model and Its Applications. Taylor & Francis Group, 2021.

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Chen, Hua Yun. Semiparametric Odds Ratio Model and Its Applications. Taylor & Francis Group, 2021.

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Book chapters on the topic "Non-parametric and semiparametric model"

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Nikulin, Mikhail, and Hong-Dar Isaac Wu. "Remarks on Computations in Parametric and Semiparametric Estimation." In The Cox Model and Its Applications, 101–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49332-8_8.

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Couallier, Vincent. "Comparison of Parametric and Semiparametric Estimates in a Degradation Model with Covariates and Traumatic Censoring." In Statistics for Industry and Technology, 81–96. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-0-8176-8206-4_6.

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Boucher, Kenneth M., Bernard Asselain, Alexander D. Tsodikov, and Andrei Y. Yakovlev. "Semiparametric Versus Parametric Regression Analysis Based on the Bounded Cumulative Hazard Model: An Application to Breast Cancer Recurrence." In Statistics for Industry and Technology, 399–415. Boston, MA: Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-0-8176-8206-4_25.

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Van Heerde, Harald J. "Non- and Semiparametric Regression Models." In International Series in Quantitative Marketing, 555–79. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53469-5_17.

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Proença, Isabel, and Axel Werwatz. "Comparing Parametric and Semiparametric Binary Response Models." In XploRe: An Interactive Statistical Computing Environment, 251–73. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4214-7_12.

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Lee, Myoung-jae. "Parametric Estimators for Multiple Equations." In Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models, 69–97. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2550-6_5.

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Elgammal, Ahmed, David Harwood, and Larry Davis. "Non-parametric Model for Background Subtraction." In Lecture Notes in Computer Science, 751–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45053-x_48.

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Basile, Roberto, and Román Mínguez. "Advances in Spatial Econometrics: Parametric vs. Semiparametric Spatial Autoregressive Models." In The Economy as a Complex Spatial System, 81–106. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65627-4_4.

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Sefian, Hind, Fatima Bahraoui, Zuhair Bahraoui, and Abdeladim Batmi. "Parametric and Non-parametric Wind Distribution Model for Tangier Region." In Lecture Notes in Electrical Engineering, 213–20. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36475-5_20.

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Tousain, Rob, and Stan van der Meulen. "Advances in Data-driven Optimization of Parametric and Non-parametric Feedforward Control Designs with Industrial Applications." In Model-Based Control:, 167–84. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0895-7_10.

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Conference papers on the topic "Non-parametric and semiparametric model"

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Zhang, Songlin, Xiaohua Tong, and Xinzhou Wang. "Proving nonsingularity of coefficient matrix in least squares normal equation of non-linear semiparametric model." In Geoinformatics 2007, edited by Jingming Chen and Yingxia Pu. SPIE, 2007. http://dx.doi.org/10.1117/12.761880.

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Pipiras, Vladas, Stefanos Kechagias, and Changryong Baek. "Semiparametric, parametric, and possibly sparse models for multivariate long-range dependence." In Wavelets and Sparsity XVII, edited by Yue M. Lu, Manos Papadakis, and Dimitri Van De Ville. SPIE, 2017. http://dx.doi.org/10.1117/12.2275101.

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Mohamad, Saad, Abdelhamid Bouchachia, and Moamar Sayed-Mouchaweh. "A non-parametric hierarchical clustering model." In 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE, 2015. http://dx.doi.org/10.1109/eais.2015.7368803.

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Yao, Y., J. Schiefer, S. van Waasen, and M. Schiek. "A non-parametric model for Ballistocardiography." In 2014 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2014. http://dx.doi.org/10.1109/ssp.2014.6884577.

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Gasparetto, Andrea, Giorgia Minello, and Andrea Torsello. "Non-parametric Spectral Model for Shape Retrieval." In 2015 International Conference on 3D Vision (3DV). IEEE, 2015. http://dx.doi.org/10.1109/3dv.2015.46.

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Liu, Renfeng, Cong Zhang, and Jinwen Tian. "Point matching based on non-parametric model." In Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), edited by Tianxu Zhang and Jianguo Liu. SPIE, 2015. http://dx.doi.org/10.1117/12.2205409.

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Corona, Enrique, Brian Nutter, and Sunanda Mitra. "Non-parametric Estimation of Mixture Model Order." In Interpretation (SSIAI). IEEE, 2008. http://dx.doi.org/10.1109/ssiai.2008.4512306.

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Ting Zhu and Peifeng Zeng. "Background subtraction based on non-parametric model." In 2015 4th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2015. http://dx.doi.org/10.1109/iccsnt.2015.7490985.

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Wong, S. K., and K. L. Schepler. "Non-steady-state optical-parametric-oscillator model." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1993. http://dx.doi.org/10.1364/oam.1993.mo.1.

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Hu, Peifeng, Zhenyu Zhang, W. K. Chan, and T. H. Tse. "Fault Localization with Non-parametric Program Behavior Model." In 2008 Eighth International Conference on Quality Software (QSIC). IEEE, 2008. http://dx.doi.org/10.1109/qsic.2008.44.

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Reports on the topic "Non-parametric and semiparametric model"

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Bouezmarni, Taoufik, Mohamed Doukali, and Abderrahim Taamouti. Copula-based estimation of health concentration curves with an application to COVID-19. CIRANO, 2022. http://dx.doi.org/10.54932/mtkj3339.

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COVID-19 has created an unprecedented global health crisis that caused millions of infections and deaths worldwide. Many, however, argue that pre-existing social inequalities have led to inequalities in infection and death rates across social classes, with the most-deprived classes are worst hit. In this paper, we derive semi/non-parametric estimators of Health Concentration Curve (HC) that can quantify inequalities in COVID-19 infections and deaths and help identify the social classes that are most at risk of infection and dying from the virus. We express HC in terms of copula function that we use to build our estimators of HC. For the semi-parametric estimator, a parametric copula is used to model the dependence between health and socio-economic variables. The copula function is estimated using maximum pseudo-likelihood estimator after replacing the cumulative distribution of health variable by its empirical analogue. For the non-parametric estimator, we replace the copula function by a Bernstein copula estimator. Furthermore, we use the above estimators of HC to derive copula-based estimators of health Gini coeffcient. We establish the consistency and the asymptotic normality of HC’s estimators. Using different data-generating processes and sample sizes, a Monte-Carlo simulation exercise shows that the semiparametric estimator outperforms the smoothed nonparametric estimator, and that the latter does better than the empirical estimator in terms of Integrated Mean Squared Error. Finally, we run an extensive empirical study to illustrate the importance of HC’s estimators for investigating inequality in COVID-19 infections and deaths in the U.S. The empirical results show that the inequalities in state’s socio-economic variables like poverty, race/ethnicity, and economic prosperity are behind the observed inequalities in the U.S.’s COVID-19 infections and deaths.

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Chen, G., Z. Chen, and P. Tzeng. A non-parametric discovery process model - A least squares approach. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2010. http://dx.doi.org/10.4095/261652.

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Rangaswamy, Muralidhar. Parametric and Model Based Adaptive Detection Algorithms for Non-Gaussian Interference Backgrounds. Fort Belvoir, VA: Defense Technical Information Center, August 1999. http://dx.doi.org/10.21236/ada369457.

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Reyes-Tagle, Gerardo, and Jorge E. Muñoz-Ayala. Debt and Economic Growth: Does Size Matter? Evidence from Dynamic Parametric and Static Non-parametric Approaches. Inter-American Development Bank, April 2023. http://dx.doi.org/10.18235/0004818.

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This paper provides new evidence on the effect of debt on economic growth through two alternative methodological approaches. On the one hand, by using a panel error correction model with a sample of 130 countries between 1980 and 2020, we found evidence of the existence of a range of debt-to-GDP ratios for which economic growth remains positive after debt surges. This threshold may lie between 32 percent and 136 percent, with optimal economic growth achieved at an 84 percent debt-to-GDP ratio for the whole sample of countries. The error correction form for the economic growth was dynamically consistent and non-linear with respect to the debt-to-GDP ratio. On the other hand, recent evidence has shown that commodity price volatility increases external debt accumulation for commodity-exporting countries. Still, there is no evidence of the effects of debt surges on these countries' economic growth. This paper provides original insights into the relationship between economic growth and the debt-to-GDP ratio for commodity and non-commodity-driven economies by employing a regression discontinuity design (RDD) approach. This method allows us to estimate differences in economic growth around an estimated threshold without assuming any specific function for the underlying relationship between the two variables. Our findings suggest that non-commodity-driven economies benefit from a higher threshold (85 percent) than commodity-exporting economies (50 percent).

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Rhodes, Anthony. Leveraging Model Flexibility and Deep Structure: Non-Parametric and Deep Models for Computer Vision Processes with Applications to Deep Model Compression. Portland State University Library, May 2020. http://dx.doi.org/10.15760/etd.7320.

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Rodríguez, Francisco. Cleaning Up the Kitchen Sink: On the Consequences of the Linearity Assumption for Cross-Country Growth Empirics. Inter-American Development Bank, January 2006. http://dx.doi.org/10.18235/0011322.

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Existing work in growth empirics either assumes linearity of the growth function or attempts to capture non-linearities by the addition of a small number of quadratic or multiplicative interaction terms. Under a more generalized failure of linearity or if the functional form taken by the non-linearity is not known ex ante, such an approach is inadequate and will lead to biased and inconsistent OLS and instrumental variables estimators. This paper uses non-parametric and semiparametric methods of estimation to evaluate the relevance of strong non-linearities in commonly used growth data sets. Our tests decisively reject the linearity hypothesis. A preponderance of our tests also rejects the hypothesis that growth is a separable function of its regressors. Absent separability, the approximation error of estimators of the growth function grows in proportion to the number of relevant dimensions, substantially increasing the data requirements necessary to make inferences about the growth effects of regressors. We show that appropriate non-parametric tests are commonly inconclusive as to the effects of policies, institutions and economic structure on growth.

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Gálvez, Julio. Household portfolio choices under (non-)linear income risk: an empirical framework. Madrid: Banco de España, September 2023. http://dx.doi.org/10.53479/33792.

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This paper develops a flexible, semi-structural framework to empirically quantify the non-linear transmission of income shocks to household portfolio choice decisions both at the extensive and intensive margins. I model stock market participation and portfolio allocation rules as age-dependent functions of persistent and transitory earnings components, wealth and unobserved taste shifters. I establish non-parametric identification and propose a tractable, simulation-based estimation algorithm, building on recent developments in the sample selection literature. Using recent waves of PSID data, I find heterogeneous income and wealth effects on both extensive and intensive margins, over the wealth and life-cycle dimensions. These results suggest that preferences are heterogeneous across the wealth distribution and over the life cycle. Moreover, in impulse response exercises, I find sizeable extensive margin responses to persistent income shocks. Finally, I find heterogeneity in participation costs across households in the wealth distribution.

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Zanoni, Wladimir, and Ailin He. Citizenship and the Economic Assimilation of Canadian Immigrants. Inter-American Development Bank, March 2021. http://dx.doi.org/10.18235/0003117.

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In this paper, we examine whether acquiring citizenship improves the economic assimilation of Canadian migrants. We took advantage of a natural experiment made possible through changes in the Canadian Citizenship Act of 2014, which extended the physical presence requirement for citizenship from three to four years. Using quasi-experimental methods, we found that delaying citizenship eligibility by one year adversely affected Canadian residents' wages. Access to better jobs explains a citizenship premium of 11 percent in higher wages among naturalized migrants. Our estimates are robust to model specifications, differing sampling windows to form the treatment and comparison groups, and whether the estimator is a non-parametric rather than a parametric one. We discuss how our findings are relevant to the optimal design of naturalization policies regarding efficiency and equity.

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Bouillon, César P., Patricia Yáñez-Pagans, and Viviane Azevedo. How Much Are We Willing To Pay to Send Poor Adolescents to School?: Simulating Changes to Mexico`s Oportunidades in Urban Areas. Inter-American Development Bank, June 2009. http://dx.doi.org/10.18235/0010918.

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Although Mexicos Conditional Cash Transfer Program Oportunidades has increased overall school enrollment, many adolescents do not attend school, especially in urban areas. This paper simulates the effects of changes in program design using a simple parametric method based on a simultaneous probability model of school attendance and child labor. The paper also provides alternative non parametric simulation results by extending Todd and Wolpins (2006) method to incorporate changes in working hours when attending school. The results indicate that eliminating or reducing school subsidies for primary education and increasing transfer for older students is a cost-effective way to raise overall school enrollment in urban areas. Increasing school attendance of 16-year-olds to 80 percent or more, however, would require a quadrupling of scholarships. This suggests that complementary interventions are needed.

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Petrova, Katerina. On the Validity of Classical and Bayesian DSGE-Based Inference. Federal Reserve Bank of New York, January 2024. http://dx.doi.org/10.59576/sr.1084.

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This paper studies large sample classical and Bayesian inference in a prototypical linear DSGE model and demonstrates that inference on the structural parameters based on a Gaussian likelihood is unaffected by departures from Gaussianity of the structural shocks. This surprising result is due to a cancellation in the asymptotic variance resulting into a generalized information equality for the block corresponding to the structural parameters. The underlying reason for the cancellation is the certainty equivalence property of the linear rational expectation model. The main implication of this result is that classical and Bayesian Gaussian inference achieve a semi-parametric efficiency bound and there is no need for a “sandwich-form” correction of the asymptotic variance of the structural parameters. Consequently, MLE-based confidence intervals and Bayesian credible sets of the deep parameters based on a Gaussian likelihood have correct asymptotic coverage even when the structural shocks are non-Gaussian. On the other hand, inference on the reduced-form parameters characterizing the volatility of the shocks is invalid whenever the structural shocks have a non-Gaussian density and the paper proposes a simple Metropolis-within-Gibbs algorithm that achieves correct large sample inference for the volatility parameters.

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