Consider the semiparametric transformation model <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\Lambda _{\theta _o}(Y)=m(X)+\varepsilon $$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <msub> <mi mathvariant="normal">Λ</mi> <msub> <mi mathvariant="italic">θ</mi> <mi>o</mi> </msub> </msub> <mrow> <mo stretchy="false">(</mo> <mi>Y</mi> <mo stretchy="false">)</mo> </mrow> <mo>=</mo> <mi>m</mi> <mrow> <mo stretchy="false">(</mo> <mi>X</mi> <mo stretchy="false">)</mo> </mrow> <mo>+</mo> <mi mathvariant="italic">ε</mi> </mrow> </math> </EquationSource> </InlineEquation>, where <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$\theta _o$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mi mathvariant="italic">θ</mi> <mi>o</mi> </msub> </math> </EquationSource> </InlineEquation> is an unknown finite dimensional parameter, the functions <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$\Lambda _{\theta _o}$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mi mathvariant="normal">Λ</mi> <msub> <mi mathvariant="italic">θ</mi> <mi>o</mi> </msub> </msub> </math> </EquationSource> </InlineEquation> and <InlineEquation ID="IEq4"> <EquationSource Format="TEX">$$m$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi>m</mi> </math> </EquationSource> </InlineEquation> are...</equationsource></equationsource></inlineequation></equationsource></equationsource></inlineequation></equationsource></equationsource></inlineequation></equationsource></equationsource></inlineequation>

This paper considers estimation of the regression function and its derivatives in nonparametric regression with fractional time series errors. We focus on investigating the properties of a kernel dependent function V (delta) in the asymptotic variance and finding closed form formula of it, where...

A data-driven bandwidth selection method for backfitting estimation of semiparametric additive models, when the parametric part is of main interest, is proposed. The proposed method is a double smoothing estimator of the mean-squared error of the backfitting estimator of the parametric terms....

This paper summarizes recent developments in non- and semiparametric regres- sion with stationary fractional time series errors, where the error process may be short-range, long-range dependent or antipersistent. The trend function in this model is estimated nonparametrically, while the...

This paper summarizes recent developments in non- and semiparametric regres- sion with stationary fractional time series errors, where the error process may be short-range, long-range dependent or antipersistent. The trend function in this model is estimated nonparametrically, while the...

This paper summarizes recent developments in non- and semiparametric regression with stationary fractional time series errors, where the error process may be short-range, long-range dependent or antipersistent. The trend function in this model is estimated nonparametrically, while the dependence...