Journal articles: 'Non-parametric and semiparametric model'

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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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Xiao, Ping, and Xinsheng Liu. "The Strong Consistency for the Estimators of Longitudinal Data in Semiparametric Regression Model with ρ ̃-Mixing Errors." Highlights in Science, Engineering and Technology 31 (February 10, 2023): 255–62. http://dx.doi.org/10.54097/hset.v31i.5151.

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Consider the following semiparametric regression model for longitudinal data with - mixing errors: , where, the response variable and the covariate vector taken from the -th subject at time , is the - mixing random variables, We establish a strong consistency for the least squares estimator of the parametric and the estimator of the non-parametric function under some mild conditions.

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Szydłowski, Arkadiusz. "TESTING A PARAMETRIC TRANSFORMATION MODEL VERSUS A NONPARAMETRIC ALTERNATIVE." Econometric Theory 36, no. 5 (May 12, 2020): 871–906. http://dx.doi.org/10.1017/s0266466619000355.

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Despite an abundance of semiparametric estimators of the transformation model, no procedure has been proposed yet to test the hypothesis that the transformation function belongs to a finite-dimensional parametric family against a nonparametric alternative. In this article, we introduce a bootstrap test based on integrated squared distance between a nonparametric estimator and a parametric null. As a special case, our procedure can be used to test the parametric specification of the integrated baseline hazard in a semiparametric mixed proportional hazard model. We investigate the finite sample performance of our test in a Monte Carlo study. Finally, we apply the proposed test to Kennan’s strike durations data.

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Bertoli, Wesley, Ricardo P. Oliveira, and Jorge A. Achcar. "A New Semiparametric Regression Framework for Analyzing Non-Linear Data." Analytics 1, no. 1 (June 16, 2022): 15–26. http://dx.doi.org/10.3390/analytics1010002.

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This work introduces a straightforward framework for semiparametric non-linear models as an alternative to existing non-linear parametric models, whose interpretation primarily depends on biological or physical aspects that are not always available in every practical situation. The proposed methodology does not require intensive numerical methods to obtain estimates in non-linear contexts, which is attractive as such algorithms’ convergence strongly depends on assigning good initial values. Moreover, the proposed structure can be compared with standard polynomial approximations often used for explaining non-linear data behaviors. Approximate posterior inferences for the semiparametric model parameters were obtained from a fully Bayesian approach based on the Metropolis-within-Gibbs algorithm. The proposed structures were considered to analyze artificial and real datasets. Our results indicated that the semiparametric models outperform linear polynomial regression approximations to predict the behavior of response variables in non-linear settings.

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Sinha, Samiran, Krishna K. Saha, and Suojin Wang. "Semiparametric approach for non-monotone missing covariates in a parametric regression model." Biometrics 70, no. 2 (February 26, 2014): 299–311. http://dx.doi.org/10.1111/biom.12159.

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Le, Tuan Hoang, and Dung Anh To. "Short-term flood forecasting with an amended semi-parametric regression ensemble model." Science and Technology Development Journal 20, K2 (June 30, 2017): 117–25. http://dx.doi.org/10.32508/stdj.v20ik2.457.

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Flood forecasting is very important research topic in disaster prevention and reduction. The characteristics of flood involve a rather complex systematic dynamic under the influence of different meteorological factors including linear and non-linear patterns. Recently there are many novel forecasting methods of improving the forecasting accuracy. This paper explores the potential and effect of the semiparametric regression to modelize flood water-level and to forecast the inundation of Mekong Delta in Vietnam. The semi-parametric regression technique is a combination of a parametric regression approach and a non-parametric regression concept. In the process of model building, three altered linear regression models are applied for the parametric component. They are stepwise multiple linear regression, partial least squares solution and multirecursive regression method. They are used to capture flood’s linear characteristics. The nonparametric part is solved by a modified estimation of a smooth function. Furthermore, some justified nonlinear regression models based on artificial neural network are also able to obtain flood’s non-linear characteristics. They help us to smooth the model's non-parametric constituent easily and quickly. The last element is the model's error. Then the semiparametric regression is used for ensemble model based on the principle component analysis technique. Flood water-level forecasting, with a lead time of one and more days, has been made by using a selected sequence of past water-level values and some relevant factors observed at a specific location. Time-series analytical method is utilized to build the model. Obtained empirical results indicate that the prediction by using the amended semi-parametric regression ensemble model is generally better than those obtained by using the other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the estimation power of the modern statistical model is reliable and auspicious. The proposed model here can be used as a promising alternative forecasting tool for flood to achieve better forecasting accuracy and to optimize prediction quality further.

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Komunjer, Ivana, and Quang Vuong. "SEMIPARAMETRIC EFFICIENCY BOUND IN TIME-SERIES MODELS FOR CONDITIONAL QUANTILES." Econometric Theory 26, no. 2 (August 18, 2009): 383–405. http://dx.doi.org/10.1017/s0266466609100038.

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We derive the semiparametric efficiency bound in dynamic models of conditional quantiles under a sole strong mixing assumption. We also provide an expression of Stein’s (1956) least favorable parametric submodel. Our approach is as follows: First, we construct a fully parametric submodel of the semiparametric model defined by the conditional quantile restriction that contains the data generating process. We then compare the asymptotic covariance matrix of the MLE obtained in this submodel with those of the M-estimators for the conditional quantile parameter that are consistent and asymptotically normal. Finally, we show that the minimum asymptotic covariance matrix of this class of M-estimators equals the asymptotic covariance matrix of the parametric submodel MLE. Thus, (i) this parametric submodel is a least favorable one, and (ii) the expression of the semiparametric efficiency bound for the conditional quantile parameter follows.

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Kim, Steven B., and Nathan Sanders. "Model Averaging with AIC Weights for Hypothesis Testing of Hormesis at Low Doses." Dose-Response 15, no. 2 (June 1, 2017): 155932581771531. http://dx.doi.org/10.1177/1559325817715314.

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For many dose–response studies, large samples are not available. Particularly, when the outcome of interest is binary rather than continuous, a large sample size is required to provide evidence for hormesis at low doses. In a small or moderate sample, we can gain statistical power by the use of a parametric model. It is an efficient approach when it is correctly specified, but it can be misleading otherwise. This research is motivated by the fact that data points at high experimental doses have too much contribution in the hypothesis testing when a parametric model is misspecified. In dose–response analyses, to account for model uncertainty and to reduce the impact of model misspecification, averaging multiple models have been widely discussed in the literature. In this article, we propose to average semiparametric models when we test for hormesis at low doses. We show the different characteristics of averaging parametric models and averaging semiparametric models by simulation. We apply the proposed method to real data, and we show that P values from averaged semiparametric models are more credible than P values from averaged parametric methods. When the true dose–response relationship does not follow a parametric assumption, the proposed method can be an alternative robust approach.

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Young, Virginia R. "Credibility Using Semiparametric Models." ASTIN Bulletin 27, no. 2 (November 1997): 273–85. http://dx.doi.org/10.2143/ast.27.2.542052.

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AbstractTo use Bayesian analysis to model insurance losses, one usually chooses a parametric conditional loss distribution for each risk and a parametric prior distribution to describe how the conditional distributions vary across the risks. A criticism of this method is that the prior distribution can be difficult to choose and the resulting model may not represent the loss data very well. In this paper, we apply techniques from nonparametric density estimation to estimate the prior. We use the estimated model to calculate the predictive mean of future claims given past claims. We illustrate our method with simulated data from a mixture of a lognormal conditional over a lognormal prior and find that the estimated predictive mean is more accurate than the linear Bühlmann credibility estimator, even when we use a conditional that is not lognormal.

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Robinson, Andrew P., Stephen E. Lane, and Guillaume Thérien. "Fitting forestry models using generalized additive models: a taper model example." Canadian Journal of Forest Research 41, no. 10 (October 2011): 1909–16. http://dx.doi.org/10.1139/x11-095.

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Nonparametric and semiparametric modelling methods are commonly applied in many fields. However, such methods have not been widely adopted in forestry, other than the most similar neighbour and nearest neighbor methods. Generalized additive modelling is a flexible semiparametric regression method that is useful when model-based prediction is the main goal and the parametric form of the model is unknown and possibly complex. Routines to fit generalized additive models (GAMs) are now readily available in much statistical software, making them an attractive option for forest modelling. Here, the use of GAMs is demonstrated by the construction of a taper model for six tree species in British Columbia, Canada. We compare the results with an existing flexible parametric taper model. We assess the performance of the models using the 0.632+ bootstrap method according to five key attributes: whole-stem volume, merchantable volume, number of logs, small-end diameter of the first log, and volume of the first log. The results show that the GAMs and the flexible taper function yielded similar accuracy for all attributes and all species.

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Sapra, Sunil. "A comparative study of parametric and semiparametric autoregressive models." International Journal of Accounting and Economics Studies 10, no. 1 (April 5, 2022): 15–19. http://dx.doi.org/10.14419/ijaes.v10i1.31978.

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Dynamic linear regression models are used widely in applied econometric research. Most applications employ linear autoregressive (AR) models, distributed lag (DL) models or autoregressive distributed lag (ARDL) models. These models, however, perform poorly for data sets with unknown, complex nonlinear patterns. This paper studies nonlinear and semiparametric extensions of the dynamic linear regression model and explores the autoregressive (AR) extensions of two semiparametric techniques to allow unknown forms of nonlinearities in the regression function. The autoregressive GAM (GAM-AR) and autoregressive multivariate adaptive regression splines (MARS-AR) studied in the paper automatically discover and incorporate nonlinearities in autoregressive (AR) models.Â Performance comparisons among these semiparametric AR models and the linear AR model are carried out via their application to Australian data on growth in GDP and unemployment using RMSE and GCV measures.Â Â

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FITRIANI, ANNA, I. GUSTI AYU MADE SRINADI, and MADE SUSILAWATI. "ESTIMASI MODEL REGRESI SEMIPARAMETRIK MENGGUNAKAN ESTIMATOR KERNEL UNIFORM (Studi Kasus: Pasien DBD di RS Puri Raharja)." E-Jurnal Matematika 4, no. 4 (November 30, 2015): 176. http://dx.doi.org/10.24843/mtk.2015.v04.i04.p108.

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Semiparametric regression model estimation is an estimation that combines both parametric and nonparametric regression model. In semiparametric regression, some of the variables are parametrics and the others are nonparametrics. Semiparametric regression is used when relationship pattern between independent and depentdent variables is half known and half unknown. Regression curve smoothing technique in nonparametric components in this study was using uniform kernel function. The optimal semiparametric regression curve estimation was obtained by optimal bandwidth. By choosing optimal bandwidth, we would obtain a smooth regression curve estimation in respect to data pattern. In choosing optimal bandwidth, we use minimum GCV as a criteria.The purpose of this study was to estimate the semiparametric regression function of dengue fever case using uniform kernel estimator. There were 6 independent variables namely age (in years) body temperature (in Celcius), heartbeat (in times/minutes) hematocryte ratio (in percent), amount of trombocyte (× 103/ul) and fever duration ( in days). Age, body temperature, heartbeat, amount of trombosyte and fever duration are parametric components and hematocryte ration is a nonparametric component. The optimal bandwidth (h) which was obtained with minimum GCVwas 0,005. The value of MSE which was obtained by using multiple linear regression analysis was 0,031 and by using semiparametric regression was 0,00437119.

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NIRMALA YANI, NI WAYAN MERRY, I. GUSTI AYU MADE SRINADI, and I. WAYAN SUMARJAYA. "APLIKASI MODEL REGRESI SEMIPARAMETRIK SPLINE TRUNCATED (Studi Kasus: Pasien Demam Berdarah Dengue (DBD) di Rumah Sakit Puri Raharja)." E-Jurnal Matematika 6, no. 1 (January 20, 2017): 65. http://dx.doi.org/10.24843/mtk.2017.v06.i01.p149.

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Semiparametric regression is a regression model that includes parametric components and nonparametric components in a model. The regression model in this research is truncated spline semiparametric regression with case studies of patients with Dengue Hemorrhagic Fever (DHF) at Puri Raharja Hospital during the period of January to March 2015. The best regression model estimation is obtained from the selection of optimal knots which has minimum Generalized Cross Validation (GCV) is. Parametric components in this research include age (years), body temperature (0C), platelets and hematocrit (%) as a nonparametric component. The minimum value of GCV is 0.03552045 achieved at the point of 39.6 knots, MSE value of 0.0296922; and the value of coefficient determination is 98.91%, obtained from semiparametric regression model truncated linear spline (order 2) with a single point of knots.

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Cebrián, Ana C., Michel Denuit, and Olivier Scaillet. "Testing for Concordance Ordering." ASTIN Bulletin 34, no. 01 (May 2004): 151–73. http://dx.doi.org/10.2143/ast.34.1.504960.

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We propose inference tools to analyse the concordance (or correlation) order of random vectors. The analysis in the bivariate case relies on tests for upper and lower quadrant dominance of the true distribution by a parametric or semiparametric model, i.e. for a parametric or semiparametric model to give a probability that two variables are simultaneously small or large at least as great as it would be were they left unspecified. Tests for its generalisation in higher dimensions, namely joint lower and upper orthant dominance, are also analysed. The parametric and semiparametric settings are based on the copula representation for multivariate distribution, which allows for disentangling behaviour of margins and dependence structure. A distance test and an intersection-union test for inequality constraints are developed depending on the definition of null and alternative hypotheses. An empirical illustration is given for US insurance claim data.

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Cebrián, Ana C., Michel Denuit, and Olivier Scaillet. "Testing for Concordance Ordering." ASTIN Bulletin 34, no. 1 (May 2004): 151–73. http://dx.doi.org/10.1017/s0515036100013933.

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We propose inference tools to analyse the concordance (or correlation) order of random vectors. The analysis in the bivariate case relies on tests for upper and lower quadrant dominance of the true distribution by a parametric or semiparametric model, i.e. for a parametric or semiparametric model to give a probability that two variables are simultaneously small or large at least as great as it would be were they left unspecified. Tests for its generalisation in higher dimensions, namely joint lower and upper orthant dominance, are also analysed. The parametric and semiparametric settings are based on the copula representation for multivariate distribution, which allows for disentangling behaviour of margins and dependence structure. A distance test and an intersection-union test for inequality constraints are developed depending on the definition of null and alternative hypotheses. An empirical illustration is given for US insurance claim data.

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Afzal, Arfan Raheen, Cheng Dong, and Xuewen Lu. "Estimation of partly linear additive hazards model with left-truncated and right-censored data." Statistical Modelling 17, no. 6 (June 30, 2017): 423–48. http://dx.doi.org/10.1177/1471082x17705993.

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In this article, we consider an additive hazards semiparametric model for left-truncated and right-censored data where the risk function has a partly linear structure, we call it the partly linear additive hazards model. The nonlinear components are assumed to be B-splines functions, so the model can be viewed as a semiparametric model with an unknown baseline hazard function and a partly linear parametric risk function, which can model both linear and nonlinear covariate effects, hence is more flexible than a purely linear or nonlinear model. We construct a pseudo-score function to estimate the coefficients of the linear covariates and the B-spline basis functions. The proposed estimators are asymptotically normal under the assumption that the true nonlinear functions are B-spline functions whose knot locations and number of knots are held fixed. On the other hand, when the risk functions are unknown non-parametric functions, the proposed method provides a practical solution to the underlying inference problems. We conduct simulation studies to empirically examine the finite-sample performance of the proposed method and analyze a real dataset for illustration.

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Le, Tuan Hoang, and Dung Anh To. "A Modified Semi-parametric Regression Model For Flood Forecasting." Science and Technology Development Journal 18, no. 2 (June 30, 2015): 95–105. http://dx.doi.org/10.32508/stdj.v18i2.1078.

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In recent years, inundation, one of natural calamities, occurs frequently and fiercely. We are sustained severe losses in the floods every year. Therefore, the development of control methods to determine, analyze, model and predict the floods is indispensable and urgent. In this paper, we propose a justified semiparametric regression model for flood water levels forecasting. The new model has three components. The first one is parametric elements of the model. They are water level, precipitation, evaporation, air-humidity and groundmoisture values, etc. There is a complex connection among these parametrics. Several innovated regression models have been offered and experimented for this complicated relationship. The second one is a non-parametric ingredient of our model. We use the Arnak S. Dalalyan et al.’s effective dimension-reduction subspace algorithm and some modified algorithms in neural networks to deal with it. They are altered back-propagation method and ameliorated cascade correlation algorithm. Besides, we also propose a new idea to modify the conjugate gradient one. These actions will help us to smooth the model’s non-parametric constituent easily and quickly. The last component is the model’s error. The whole elements are essential inputs to operational flood management. This work is usually very complex owing to the uncertain and unpredictable nature of underlying phenomena. Flood-waterlevels forecasting, with a lead time of one and more days, was made using a selected sequence of past water-level values observed at a specific location. Time-series analytical method is also utilized to build the model. The results obtained indicate that, with a new semiparametric regression one and the effective dimension-reduction subspace algorithm, together with some improved algorithms in neural network, the estimation power of the modern statistical model is reliable and auspicious, especially for flood forecasting/modeling.

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Gao, Jiti. "Adaptive parametric test in a semiparametric regression model." Communications in Statistics - Theory and Methods 26, no. 4 (January 1997): 787–800. http://dx.doi.org/10.1080/03610929708831950.

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Xu, Jingtian, Haijun Huang, and Xiong Pan. "Parametric solution of p-norm semiparametric regression model." Multimedia Tools and Applications 78, no. 21 (December 5, 2018): 30127–39. http://dx.doi.org/10.1007/s11042-018-6918-0.

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MacEachern, Steven N., and Subharup Guha. "Parametric and semiparametric hypotheses in the linear model." Canadian Journal of Statistics 39, no. 1 (February 24, 2011): 165–80. http://dx.doi.org/10.1002/cjs.10091.

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ADRIANINGSIH, NARITA YURI, and ANDREA TRI RIAN DANI. "ESTIMASI MODEL REGRESI SEMIPARAMETRIK SPLINE TRUNCATED MENGGUNAKAN METODE MAXIMUM LIKELIHOOD ESTIMATION (MLE)." Jambura Journal of Probability and Statistics 2, no. 2 (October 20, 2021): 56–63. http://dx.doi.org/10.34312/jjps.v2i2.10255.

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Regression modeling with a semiparametric approach is a combination of two approaches, namely the parametric regression approach and the nonparametric regression approach. The semiparametric regression model can be used if the response variable has a known relationship pattern with one or more of the predictor variables used, but with the other predictor variables the relationship pattern cannot be known with certainty. The purpose of this research is to examine the estimation form of the semiparametric spline truncated regression model. Suppose that random error is assumed to be independent, identical, and normally distributed with zero mean and variance , then using this assumption, we can estimate the semiparametric spline truncated regression model using the Maximum Likelihood Estimation (MLE) method. Based on the results, the estimation results of the semiparametric spline truncated regression model were obtained p=(inv(M'M)) M'y

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Berry, Tyrus, and John Harlim. "Semiparametric modeling: Correcting low-dimensional model error in parametric models." Journal of Computational Physics 308 (March 2016): 305–21. http://dx.doi.org/10.1016/j.jcp.2015.12.043.

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Kangas, Annika, Mari Myllymäki, Terje Gobakken, and Erik Næsset. "Model-assisted forest inventory with parametric, semiparametric, and nonparametric models." Canadian Journal of Forest Research 46, no. 6 (June 2016): 855–68. http://dx.doi.org/10.1139/cjfr-2015-0504.

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Survey sampling with model-assisted estimation has been gaining popularity in forest inventory recently, as the availability of cheap, good-quality remotely sensed data that can be used as auxiliary information has improved. Most of the studies have been carried out using parametric (linear or nonlinear) models. However, nonparametric and semiparametric models such as k nearest neighbor, kernel, and generalized additive are widely used in forest inventory. The results are usually calculated using the difference estimator (i.e., assuming an external model), even though the models used are based on the sample (i.e., an internal model). In that case, variances will likely be underestimated. In this study, we analyze how well the difference estimator works for different types of models, both internal and external. The study is based on simulated populations produced using a C-vine copula model with empirical marginals. The external model is based on real data, and the internal models are estimated from samples from the simulated population. The results show that the analytical variance estimates for a difference estimator based on an overfitted kernel model can seriously underestimate the true variance.

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Song, Xin-Yuan, Ye-Mao Xia, Jun-Hao Pan, and Sik-Yum Lee. "Model Comparison of Bayesian Semiparametric and Parametric Structural Equation Models." Structural Equation Modeling: A Multidisciplinary Journal 18, no. 1 (January 13, 2011): 55–72. http://dx.doi.org/10.1080/10705511.2011.532720.

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Wei, Chuanhua, Ran Yan, and Tao Tao. "Statistical Inference on Semiparametric Spatial Additive Model." Journal of Mathematics Research 12, no. 2 (February 19, 2020): 1. http://dx.doi.org/10.5539/jmr.v12n2p1.

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There has been a growing interest on using nonparametric and semiparametric modelling techniques for the analysis of spatial data because of their powerfulness in extracting the underlying local patterns in the data. In this study, stimulated by the Boston house price data, we apply a semiparametric spatial additive model to incorporation of spatial e ects in regression models. For this semiparametric model, we develop a linear hypothesis test of parametric coecients as well as a test for the existence of the spatial e ects. For the problem of variable selection, the adaptive Lasso method was applied. Monte Carlo simulation studies are conducted to illustrate the finite sample performance of the proposed inference procedures. Finally, an application in Boston housing data is studied.

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Kato, Risa, and Takayuki Shiohama. "Model and Variable Selection Procedures for Semiparametric Time Series Regression." Journal of Probability and Statistics 2009 (2009): 1–37. http://dx.doi.org/10.1155/2009/487194.

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Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.

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Pathiravasan, Chathurangi H., and Bhaskar Bhattacharya. "A Semiparametric Tilt Optimality Model." Stats 6, no. 1 (December 22, 2022): 1–16. http://dx.doi.org/10.3390/stats6010001.

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Practitioners often face the situation of comparing any set of k distributions, which may follow neither normality nor equality of variances. We propose a semiparametric model to compare those distributions using an exponential tilt method. This extends the classical analysis of variance models when all distributions are unknown by relaxing its assumptions. The proposed model is optimal when one of the distributions is known. Large-sample estimates of the model parameters are derived, and the hypotheses for the equality of the distributions are tested for one-at-a-time and simultaneous comparison cases. Real data examples from NASA meteorology experiments and social credit card limits are analyzed to illustrate our approach. The proposed approach is shown to be preferable in a simulated power comparison with existing parametric and nonparametric methods.

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Verotta, D., S. L. Beal, and L. B. Sheiner. "Semiparametric approach to pharmacokinetic-pharmacodynamic data." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 256, no. 4 (April 1, 1989): R1005—R1010. http://dx.doi.org/10.1152/ajpregu.1989.256.4.r1005.

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A semiparametric model for analysis of pharmacokinetic (PK) and pharmacodynamic (PD) data arising from non-steady-state experiments is presented. The model describes time lag between drug concentration in a sampling compartment, e.g., venous blood (Cv), and drug effect (E). If drug concentration at the effect site (Ce) equilibrates with arterial blood concentration (Ca) slower than with Cv, a non-steady-state experiment yields E vs. Cv data describing a counterclockwise hysteresis loop. If Ce equilibrates with Ca faster than with Cv, clockwise hysteresis is observed. To model hysteresis, a parametric model is proposed linking (unobserved) Ca to Cv with elimination rate constant kappa ov and also linking Ca to Ce with elimination rate constant kappa oe. When kappa oe is greater than (or less than) kappa ov clockwise (or counterclockwise) hysteresis occurs. Given kappa oe and kappa ov, numerical (constrained) deconvolution is used to obtain the disposition function of the arterial compartment (Ha), and convolution is used to calculate Ce given Ha. The values of kappa oe and kappa ov are chosen to collapse the hysteresis loops to single curves representing the Ce-E (steady-state) concentration-response curve. Simulations, and an application to real data, are reported.

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Bult, Jan Roelf. "Semiparametric versus Parametric Classification Models: An Application to Direct Marketing." Journal of Marketing Research 30, no. 3 (August 1993): 380–90. http://dx.doi.org/10.1177/002224379303000309.

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In this paper we are concerned with estimation of a classification model using semiparametric and parametric methods. Benefits and limitations of semiparametric models in general, and of Manski's maximum score method in particular, are discussed. The maximum score method yields consistent estimates under very weak distributional assumptions. The maximum score method can very easily be used in situations where it is more serious to make one kind of classification error than another. In this paper, we use a so-called threshold-crossing model to discriminate between credit card holders and nonholders. The estimated parameters of the logit model differ significantly from the estimates of maximum score. Given an asymmetric loss function, maximum score performs better than the logit model.

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Wang, Liang, Tingjia Xu, Longhao Qin, and Chenge Liu. "Research on the Value at Risk of Basis for Stock Index Futures Hedging in China Based on Two-State Markov Process and Semiparametric RS-GARCH Model." Discrete Dynamics in Nature and Society 2019 (June 2, 2019): 1–15. http://dx.doi.org/10.1155/2019/8904162.

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This article aims to investigate the Value at Risk of basis for stock index futures hedging in China. Since the RS-GARCH model can effectively describe the state transition of variance in VaR and the two-state Markov process can significantly reduce the dimension, this paper constructs the parameter and semiparametric RS-GARCH models based on two-state Markov process. Furthermore, the logarithm likelihood function method and the kernel estimation with invariable bandwidth method are used for VaR estimation and empirical analysis. It is found that the three fitting errors (MSE, MAD, and QLIKE) of conditional variance calculated by semiparametric model are significantly smaller than that of the parametric model. The results of Kupiec backtesting on VaR obtained by the two models show that the failure days of the former are less than or equal to that of the latter, so it can be inferred that the semiparametric RS-GARCH model constructed in this paper is more effective in estimating the Value at Risk of the basis for Chinese stock index futures. In addition, the mean value and standard deviation of VaR obtained by the semiparametric RS-GARCH model are smaller than that of the parametric method, which can prove that the former model is more conservative in risk estimation.

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Jenish, Nazgul. "SPATIAL SEMIPARAMETRIC MODEL WITH ENDOGENOUS REGRESSORS." Econometric Theory 32, no. 3 (December 18, 2014): 714–39. http://dx.doi.org/10.1017/s0266466614000905.

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This paper proposes a semiparametric generalized method of moments estimator (GMM) estimator for a partially parametric spatial model with endogenous spatially dependent regressors. The finite-dimensional estimator is shown to be consistent and root-n asymptotically normal under some reasonable conditions. A spatial heteroscedasticity and autocorrelation consistent covariance estimator is constructed for the GMM estimator. The leading application is nonlinear spatial autoregressions, which arise in a wide range of strategic interaction models. To derive the asymptotic properties of the estimator, the paper also establishes a stochastic equicontinuity criterion and functional central limit theorem for near-epoch dependent random fields.

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Guan, Biing T., Shih-Hao Weng, Shing-Rong Kuo, Tsung-Yi Chang, Hsin-Wu Hsu, and Chieh-Wen Shen. "Analyzing the effects of stand thinning on microclimates with semiparametric smoothing splines." Canadian Journal of Forest Research 36, no. 7 (July 1, 2006): 1641–48. http://dx.doi.org/10.1139/x06-057.

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Monitoring the effects of stand thinning on microclimates is an integral part of any thinning experiment. It is through its modifications of microclimates that thinning alters important ecological processes. An efficient analysis of microclimate-monitoring data should address both the effects of thinning regimes on, and the temporal response trends of, microclimates. Probably because of the difficulties in modeling temporal trends parametrically, an examination of the existing literature on thinning showed that only a few studies have attempted to address the second aspect. We propose the use of semiparametric smoothing splines to analyze monitoring data from thinning experiments. First, the concept of a smoothing spline is briefly described. We then provide an example in which semiparametric mixed-effects smoothing-spline models were used to analyze microclimate-monitoring data from a thinning experiment. The proposed approach not only successfully detected the effects of thinning, but also revealed interesting temporal trends. For each of the microclimatic variables, we also compared the performance of the fitted semiparametric model with that of a parametric model. In general, the semiparametric model performed better than its parametric counterpart. We also addresse some concerns in using the proposed approach.

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Husain, Hartina, I. N. Budiantara, and Ismaini Zain. "Mixed estimator of spline truncated, Fourier series, and kernel in biresponse semiparametric regression model." IOP Conference Series: Earth and Environmental Science 880, no. 1 (October 1, 2021): 012046. http://dx.doi.org/10.1088/1755-1315/880/1/012046.

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Abstract Regression analysis is a method of analysis to determine the relationship between the response and the predictor variables. There are three approaches in regression analysis, namely the parametric, nonparametric, and semiparametric approaches. Biresponse Semiparametric regression model is a regression model that uses a combination approach between parametric and nonparametric components, where two response variables are correlated with each other. For data cases with several predictor variables, different estimation technique approaches can be used for each variable. In this study, the parametric component is assumed to be linear. At the same time, the nonparametric part is approached using a mixture of three estimation techniques, namely, spline truncated, Fourier series, and the kernel. The unknown data pattern is assumed to follow the criteria of each of these estimation techniques. The spline is used when the data pattern tends to change at certain time intervals, the Fourier series is used when the data pattern tends to repeat itself, and the kernel is used when the data does not have a specific way. This study aims to obtain parameter estimates for the mixed semiparametric regression model of spline truncated, Fourier series, and the kernel on the biresponse data using the Weighted Least Square (WLS) method. The formed model depends on the selection of knot points, oscillation parameters, and optimal bandwidth. The best model is based on the smallest Generalized Cross Validation (GCV).

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Feng, Aifen, Jingya Fan, Zhengfen Jin, Mengmeng Zhao, and Xiaogai Chang. "Research Based on High-Dimensional Fused Lasso Partially Linear Model." Mathematics 11, no. 12 (June 16, 2023): 2726. http://dx.doi.org/10.3390/math11122726.

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In this paper, a partially linear model based on the fused lasso method is proposed to solve the problem of high correlation between adjacent variables, and then the idea of the two-stage estimation method is used to study the solution of this model. Firstly, the non-parametric part of the partially linear model is estimated using the kernel function method and transforming the semiparametric model into a parametric model. Secondly, the fused lasso regularization term is introduced into the model to construct the least squares parameter estimation based on the fused lasso penalty. Then, due to the non-smooth terms of the model, the subproblems may not have closed-form solutions, so the linearized alternating direction multiplier method (LADMM) is used to solve the model, and the convergence of the algorithm and the asymptotic properties of the model are analyzed. Finally, the applicability of this model was demonstrated through two types of simulation data and practical problems in predicting worker wages.

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Aydın, Dursun, Syed Ejaz Ahmed, and Ersin Yılmaz. "Right-Censored Time Series Modeling by Modified Semi-Parametric A-Spline Estimator." Entropy 23, no. 12 (November 27, 2021): 1586. http://dx.doi.org/10.3390/e23121586.

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This paper focuses on the adaptive spline (A-spline) fitting of the semiparametric regression model to time series data with right-censored observations. Typically, there are two main problems that need to be solved in such a case: dealing with censored data and obtaining a proper A-spline estimator for the components of the semiparametric model. The first problem is traditionally solved by the synthetic data approach based on the Kaplan–Meier estimator. In practice, although the synthetic data technique is one of the most widely used solutions for right-censored observations, the transformed data’s structure is distorted, especially for heavily censored datasets, due to the nature of the approach. In this paper, we introduced a modified semiparametric estimator based on the A-spline approach to overcome data irregularity with minimum information loss and to resolve the second problem described above. In addition, the semiparametric B-spline estimator was used as a benchmark method to gauge the success of the A-spline estimator. To this end, a detailed Monte Carlo simulation study and a real data sample were carried out to evaluate the performance of the proposed estimator and to make a practical comparison.

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Soberon, Alexandra, and Irene D’Hers. "The Environmental Kuznets Curve: A Semiparametric Approach with Cross-Sectional Dependence." Journal of Risk and Financial Management 13, no. 11 (November 23, 2020): 292. http://dx.doi.org/10.3390/jrfm13110292.

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This paper proposes a new approach to examine the relationship between CO2 emissions and economic developing. In particular, we propose to test the Environmental Kuznets Curve (EKC) hypothesis for a panel of 24 OECD countries and 32 non-OECD countries by developing a more flexible estimation technique which enables to account for functional form misspecification, cross-sectional dependence, and heterogeneous relationships among variables, simultaneously. We propose a new nonparametric estimator that extends the well-known Common Correlated Effect (CCE) approach from a fully parametric framework to a semiparametric panel data model. Our results corroborates that the nature and validity of the income–pollution relationship based on the EKC hypothesis depends on the model assumptions about the functional form specification. For all the countries analyzed, the proposed semiparametric estimator leads to non-monotonically increasing or decreasing relationships for CO2 emissions, depending on the level of economic development of the country.

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Lin, Feng-Chang, Jianwen Cai, Jason P. Fine, Elisabeth P. Dellon, and Charles R. Esther. "Semiparametric estimation of the proportional rates model for recurrent events data with missing event category." Statistical Methods in Medical Research 30, no. 7 (June 18, 2021): 1624–39. http://dx.doi.org/10.1177/09622802211023975.

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Proportional rates models are frequently used for the analysis of recurrent event data with multiple event categories. When some of the event categories are missing, a conventional approach is to either exclude the missing data for a complete-case analysis or employ a parametric model for the missing event type. It is well known that the complete-case analysis is inconsistent when the missingness depends on covariates, and the parametric approach may incur bias when the model is misspecified. In this paper, we aim to provide a more robust approach using a rate proportion method for the imputation of missing event types. We show that the log-odds of the event type can be written as a semiparametric generalized linear model, facilitating a theoretically justified estimation framework. Comprehensive simulation studies were conducted demonstrating the improved performance of the semiparametric method over parametric procedures. Multiple types of Pseudomonas aeruginosa infections of young cystic fibrosis patients were analyzed to demonstrate the feasibility of our proposed approach.

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Mays, James E., Jeffrey B. Birch, and B. Alden Starnes. "Model robust regression: combining parametric, nonparametric, and semiparametric methods." Journal of Nonparametric Statistics 13, no. 2 (January 2001): 245–77. http://dx.doi.org/10.1080/10485250108832852.

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Poudel, Biswo N., Krishna P. Paudel, and Keshav Bhattarai. "Searching for an Environmental Kuznets Curve in Carbon Dioxide Pollutant in Latin American Countries." Journal of Agricultural and Applied Economics 41, no. 1 (April 2009): 13–27. http://dx.doi.org/10.1017/s1074070800002522.

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This study utilized a semiparametric panel model to estimate environmental Kuznets curves (EKC) for carbon dioxide (CO2) in 15 Latin American countries, using hitherto unused data on forestry acreage in each country. Results showed an N-shaped curve for the region; however, the shape of the curve is sensitive to the removal of some groups of countries. Specification tests support a semiparametric panel model over a parametric quadratic specification.

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Adhya, Sumanta. "Bootstrap Variance Estimation for Semiparametric Finite Population Distribution Function Estimator." Calcutta Statistical Association Bulletin 70, no. 1 (May 2018): 17–32. http://dx.doi.org/10.1177/0008068318765583.

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Estimating finite population distribution function (FPDF) emerges as an important problem to the survey statisticians since the pioneering work of Chambers and Dunstan [1] . It unifies estimation of standard finite population parameters, namely, mean and quantiles. Regarding this, estimating variance of FPDF estimator is an important task for accessing the quality of the estimtor and drawing inferences (e.g., confidence interval estimation) on finite population parameters. Due to non-linearity of FPDF estimator, resampling-based methods are developed earlier for parametric or non-parametric Chambers–Dunstan estimator. Here, we attempt the problem of estimating variance of P-splines-based semiparametric model-based Chambers–Dunstan type estimator of the FPDF. The proposed variance estimator involes bootstrapping. Here, the bootstrap procedure is non-trivial since it does not imitate the full mechanism of two-stage sample generating procedure from an infinite hypothetical population (superpopulation). We have established the weak consistency of the proposed resampling-based variance estimator for specific sampling designs, e.g., simple random sampling. Also, the satisfactory empirical performance of the poposed estimator has been shown through simulation studies and a real life example.

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Koech, Ben Kiprono. "Estimation of Receiver Operating Characteristic Surface Using Mixtures of Finite Polya Trees (MFPT)." International Journal of Statistics and Probability 10, no. 2 (January 25, 2021): 18. http://dx.doi.org/10.5539/ijsp.v10n2p18.

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Generalisation of Receiver operating characteristic (ROC) curve has become increasingly useful in evaluating the performance of diagnostic tests that have more than binary outcomes. While parametric approaches have been widely used over the years, the limitations associated with parametric assumptions often make it difficult to modelling the volume under surface for data that do not meet criteria under parametric distributions. As such, estimation of ROC surface using nonparametric approaches have been proposed to obtained insights on available data. One of the common approaches to non-parametric estimation is the use of Bayesian models where assumptions about priors can be made then posterior distributions obtained which can then be used to model the data. This study uses Polya tree priors where mixtures of Polya trees approach was used to model simulated three-way ROC data. The results of VUS estimation which is considered a suitable inference in evaluating performance of a diagnostic test, indicated that the mixtures of Polya trees model fitted well the ROC surface data. Further, the model performed relatively well compared to parametric and semiparametric models under similar assumptions.  

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

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