Semi-Parametric and Non-Linear Functional Forms
Semi and non semi parametric models for reliability analysis
... where j 0 are the unconstrained partial likelihood estimates. The value of u chosen by Generalized Cross-Validation (GCV) statistics suggested by Wahba (1980). To construct this statistic, we need a linear ...
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Non-parametric and semi-parametric estimation of spatial covariance function
... Statisticians have recognized the necessity to model nonstationary spatial random processes and have proposed different methodologies on this topic. Haas (1990) used a moving window approach to model acid deposition, ...
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Dynamic semi-parametric factor model for functional expectiles
... and non-stationarity can arise from differ- ent sources, we start with an overparametrized model, which captures almost any behaviour or trend, ...the linear or quadratic trend as ...
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Essays on semi-/non-parametric methods in econometrics
... Even if a nonparametric model is attractive for its exibility, it typically requires a larger sample than a parametric model to obtain estimators of reason- able precision. Moreover, the rate of convergence is ...
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Engel Flexibility in Household Budget Studies: Non-parametric Evidence versus Standard Functional Forms
... 4. Concluding Remarks and Research Perspective The non-parametric evidence in Figure 3.2 strongly suggests that currently widely used consumer demand systems have Engel specifications which lack sufficient ...
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Semi-parametric estimation of partially linear single-index models
... Based on this model, the effects of weather conditions on the CR problems are as follows. The coefficients of temperatures x 5,t −2 and x 5,t −5 forms a contrast. Together with Fig. 6, it suggests that a rapid ...
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Application of Parametric, Semi-Parametric and Non-Parametric Survival Models for Myocardial Infarction (Mi) Patients
... REFERENCES 1. Cox, D.R and Oakes, D. (1984), Analysis of Survival Data. London Chapman and Hall. 2. Introduction to linear regression analysis 5 th edition (2012), Douglas C. Montgomery, Elizabeth A. Peck, G. ...
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A (semi-)parametric functional coefficient autoregressive conditional duration model
... (1998) linear autoregressive conditional duration (ACD) model that abound in the litera- ...our functional coefficient autoregressive conditional duration (FC-ACD) model not only nests the ACD-type ...
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A non parametric approach for calibration with functional data
... a functional space (functional calibration); the experimental design can be fixed (the Y values are not random but set by the researcher) or random (Y is a random variable as well as X); and the nature of ...
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Parametric design to reduced-order functional observer for linear timevarying
... The functional observer (FO) aims at observing the linear combination of state variables and has been widely used in practical ...for linear time-invariant (LTI) systems, which can also be used in ...
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A (Semi)Parametric Functional Coefficient Logarithmic Autoregressive Conditional Duration Model
... the linear autoregressive conditional duration (ACD) model that abound in the literature (Engle and Russell, ...our functional coefficient logarithmic ACD (FC-LACD) model not only nests the ACD-type ...
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Semi-linear diffusive representations for non-linear fractional differential systems
... of non-linear fractional di erential equations is ...the non-linearity is given for the input-output stability, thanks to many di erent reformulations of the system using di usive rep- resentations ...
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"Asymptotic Expansions of the Distributions of Semi-Parametric Estimators in a Linear Simultaneous Equations System"
... of semi-parametric estimators for the coefficients of a single structural equation in the linear simultaneous equations ...the non-centrality ...the linear simultaneous equations by ...
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Non-Parametric and Semi-Parametric RSSI/Distance Modeling for Target Tracking in Wireless Sensor Networks
... a non- parametric regression model, while the second one is a semi-parametric regression model that combines the well-known log-distance theoretical propagation model with a ...
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Non-Parametric Estimation of Forecast Distributions in Non-Gaussian, Non-linear State Space Models
... of non-Gaussian time series variables in the context of a general non-Gaussian, non-linear state space ...A non-parametric …lter is derived that exploits the functional ...
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Balancing flexibility and robustness in machine learning: semi-parametric methods and sparse linear models
... standard parametric or purely non-parametric approaches in ...isolation. Semi-parametric methods include both parametric and non-parametric components in the models ...
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Nonparametric time series prediction: A semi-functional partial linear modeling
... should be added to the right-hand side in (20) (we have de- noted s = r/(ar − 2a + r)). Remark 3. Theorem 3 extends the asymptotic normality of the regression function estimator in the pure nonparametric ...
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Efficiency Analysis of Rural Hospitals: Parametric and Semi-parametric Approaches
... the semi-parametric model defined by Simar and Wilson (2007), the assumptions of a linear functional form and truncated normal errors in the second stage appear to be less restrictive as ...
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Semi- and non-parametric flood frequency analysis
... 4.6 Conclusion and outlook The analysis of trends in hydrological time series is motivated by a changing climate and by anthropogenic interference with nature, for instance, the dynamic process of urban- ization during ...
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Comparison of Parametric (OLS) and Non-Parametric (THEIL’S) Linear Regression
... of parametric and non-parametric linear ...its non-parametric ...the parametric OLS is better than its non- parametric Theil’s regression since their AIC and ...
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