Bandwidth selection in marker dependent kernel hazard estimation

Pérez, M. Luz Gámiz; Janys, Lena; Miranda, María Dolores Martínez; Nielsen, Jens Perch

doi:10.1016/j.csda.2013.06.010

Cited by 15 publications

(2 citation statements)

References 25 publications

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“…A possible generalisation of our approach would be the use of the Do-validation principle to obtain more robust validated R 2 -values. See Mammen et al (2011) and Gámiz-Pérez et al (2013) for some recent improvement of the cross-validation approach. Since maximizing the R 2 V is equivalent to minimize the cross-validation criterion, we also use the R 2 V to find the optimal prediction bandwidth for the kernel function used (Quartic kernel).…”

Section: The Prediction Frameworkmentioning

confidence: 99%

Nonparametric long term prediction of stock returns with generated bond yields

Scholz

Sperlich

Nielsen³

2016

Insurance: Mathematics and Economics

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Recent empirical approaches in forecasting equity returns or premiums found that dynamic interactions among the stock and bond are relevant for long term pension products. Automatic procedures to upgrade or downgrade risk exposure could potentially improve long term performance for such products. The risk and return of bonds is more easy to predict than the risk and return of stocks. This and the well known stock-bond correlation motivates the inclusion of the current bond yield in a model for the prediction of excess stock returns. Here, we take the actuarial long term view using yearly data, and focus on nonlinear relationships between a set of covariates. We employ fully nonparametric models and apply for estimation a local-linear kernel smoother. Since the current bond yield is not known, it is predicted in a prior step. The structure imposed this way in the final estimation process helps to circumvent the curse of dimensionality and reduces bias in the estimation of excess stock returns. Our validated stock prediction results show that predicted bond returns improve stock prediction significantly.

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Section: The Prediction Frameworkmentioning

confidence: 99%

Nonparametric long term prediction of stock returns with generated bond yields

Scholz

Sperlich

Nielsen³

2016

Insurance: Mathematics and Economics

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“…The case of kernel hazard estimation is studied in Nielsen & Tanggaard (2001). A multivariate study is given in Gámiz Pérez, Janys, Martínez Miranda & Nielsen (2013a). However, none of these papers use the GPA technique introduced in this paper but rather stop with local adjustments.…”

Section: Introductionmentioning

confidence: 99%

Global Polynomial Kernel Hazard Estimation

Hiabu

Martínez-Miranda

Nielsen

et al. 2015

Rev. colomb. estad.

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This paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator. The estimator works well from a theoretical point of view as it asymptotically reduces bias with unchanged variance. A simulation study investigates the finite-sample properties of GPA. The method is tested on local constant and local linear estimators. From the simulation experiment we conclude that the global estimator improves the goodness-of-fit. An especially encouraging result is that the bias-correction works well for small samples, where traditional bias reduction methods have a tendency to fail.Key words: Kernel estimation; hazard function; local linear estimation; boundary kernels; polynomial correction. ResumenEn este artículo se introduce un nuevo método de correción del sesgo para la estimación núcleo de la función de riesgo. El método, denominado ajuste polinomial global (APG), consiste en una corrección global que es aplicable a cualquier tipo de estimador núcleo de la función de riesgo. Se comprueba que APG posee buenas propiedades asintóticas y que consigue reducir el sesgo sin incrementar la varianza. Se realizan estudios de simulación para evaluar las propiedades del APG en muestras finitas. Dichos estudios muestran un buen comportamiento en la práctica del APG. Esto es especialmente alentador dado que para muestras finitas los métodos tradicionales de reducción del sesgo tienden a tener un comportamiento bastante pobre.Palabras clave: Estimación kernel; funciones de riesgo; estimación local lineal; kernels de frontera, corrección polinomial.----------

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One-sided cross-validation for nonsmooth density functions

Savchuk

2019

Comput Stat

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One-sided cross-validation (OSCV) is a bandwidth selection method initially introduced by Hart and Yi (1998) in the context of smooth regression functions. Martínez-Miranda et al. (2009) developed a version of OSCV for smooth density func-tions. This article extends the method for nonsmooth densities. It also introduces the fully robust OSCV modification that produces consistent OSCV bandwidths for both smooth and nonsmooth cases. Practical implementations of the OSCV method for smooth and nonsmooth densities are discussed. One of the considered cross-validation kernels has potential for improving the OSCV method's implementation in the regression context.

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Bandwidth selection in marker dependent kernel hazard estimation

Cited by 15 publications

References 25 publications

Nonparametric long term prediction of stock returns with generated bond yields

Nonparametric long term prediction of stock returns with generated bond yields

Global Polynomial Kernel Hazard Estimation

One-sided cross-validation for nonsmooth density functions

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