Selección de la ventana en suavización tipo núcleo de la parte no parametríca de un modelo parcialmente lineal con errores autorregresivos

Supongamos que yi = ζiT β + m(ti) + εi, i = 1, ..., n, donde el vector (p x 1) β y la función m(·) son desconocidos, y los errores εi provienen de un proceso autorregresivo de orden uno (AR(1)) estacionario. Discutimos aquí el problema de la selección del parámetro ventana de un estimador tipo núcleo de la función m(·) basado en un estimador Generalizado de Mínimos Cuadrados de β. Obtenemos la expresión asintótica de una ventana óptima y proponemos un método para estimarla, de modo que dé lugar a un estimador óptimo de m(·). Los resultados obtenidos generalizan aquellos obtenidos por Quintela (1994b) en regresión no paramétrica

Using robust FPCA to identify outliers in functional time series, with applications to the electricity market

This study proposes two methods for detecting outliers in functional time series. Both methods take dependence in the data into account and are based on robust functional principal component analysis. One method seeks outliers in the series of projections on the first principal component. The other obtains uncontaminated forecasts for each data set and determines that those observations whose residuals have an unusually high norm are considered outliers. A simulation study shows the performance of these proposed procedures and the need to take dependence in the time series into account. Finally, the usefulness of our methodology is illustrated in two real datasets from the electricity market: daily curves of electricity demand and price in mainland Spain, for the year 2012

Fast Algorithm for Impact Point Selection in Semiparametric Functional Models

[Abstract] A new sparse semiparametric functional model is proposed, which tries to incorporate the influence of two functional variables in a scalar response in a quite simple and interpretable way. One of the functional variables is included trough a single-index structure and the other one linearly, but trough the high-dimensional vector of its discretized observations. For this model, a new algorithm for impact point selection in the linear part and for the model estimation is proposed. This procedure is based on the functional origin of the linear covariates. Some asymptotic results will ensure the good performance of the method. The computational efficiency of the algorithm, without loss of predictive power, will be showed trough a simulation study and a real data application, by comparing its results with those obtained trough the standard PLS method.Ministerio de Economía y Competitividad; MTM2014-52876-RMinisterio de Economía y Competitividad; MTM2017-82724-RXunta de Galicia; ED431G/01 2016-2019Xunta de Galicia; ED431C2016-015Xunta de Galicia; ED481A-2018/19

Modèle non paramétrique parcimonieux pour la détection des points d'impact d'une variable fonctionnelle

AbstractA nonlinear sparse model is defined for selecting impact points in regression problems with functional predictors, and a variable selection procedure based on screening and splitting is proposed. Some asymptotics are stated both for the impact points and for the parameters of the model

Selección de la ventana en suavización tipo núcleo de la parte no parametríca de un modelo parcialmente lineal con errores autorregresivos

Supongamos que yi = ζiT β + m(ti) + εi, i = 1, ..., n, donde el vector (p x 1) β y la función m(·) son desconocidos, y los errores εi provienen de un proceso autorregresivo de orden uno (AR(1)) estacionario. Discutimos aquí el problema de la selección del parámetro ventana de un estimador tipo núcleo de la función m(·) basado en un estimador Generalizado de Mínimos Cuadrados de β. Obtenemos la expresión asintótica de una ventana óptima y proponemos un método para estimarla, de modo que dé lugar a un estimador óptimo de m(·). Los resultados obtenidos generalizan aquellos obtenidos por Quintela (1994b) en regresión no paramétrica

Selección de la ventana en suavización tipo núcleo de la parte no paramétrica de un modelo parcialmente lineal con errores autorregresivos

Supongamos que yi = ?iT ß + m(ti) + ei, i = 1, ..., n, donde el vector (p x 1) ß y la función m(·) son desconocidos, y los errores ei provienen de un proceso autorregresivo de orden uno (AR(1)) estacionario. Discutimos aquí el problema de la selección del parámetro ventana de un estimador tipo núcleo de la función m(·) basado en un estimador Generalizado de Mínimos Cuadrados de ß. Obtenemos la expresión asintótica de una ventana óptima y proponemos un método para estimarla, de modo que dé lugar a un estimador óptimo de m(·). Los resultados obtenidos generalizan aquellos obtenidos por Quintela (1994b) en regresión no paramétric

On bandwidth selection in partial linear regression models under dependence

We obtain the expression of an asymptotically optimal bandwidth for a semiparametric least-squares estimator of [beta] in the model y=xT[beta]+m(t)+[var epsilon], where x is random, t is fixed, m is unknown and [var epsilon] is strong mixing. The selection method is based on second-order approximations for the variance and bias. Asymptotic normality is also established.Partial linear models Kernel smoothing Bandwidth selection Mixing

Nonparametric time series prediction: A semi-functional partial linear modeling

There is a recent interest in developing new statistical methods to predict time series by taking into account a continuous set of past values as predictors. In this functional time series prediction approach, we propose a functional version of the partial linear model that allows both to consider additional covariates and to use a continuous path in the past to predict future values of the process. The aim of this paper is to present this model, to construct some estimates and to look at their properties both from a theoretical point of view by means of asymptotic results and from a practical perspective by treating some real data sets. Although the literature on the use of parametric or nonparametric functional modeling is growing, as far as we know, this is the first paper on semiparametric functional modeling for the prediction of time series.Partial linear regression Functional data Semiparametric functional model Dependent data Time series prediction

Sparse Semi-Functional Partial Linear Single-Index Regression

The variable selection problem is studied in the sparse semi-functional partial linear model, with single-index type influence of the functional covariate in the response. The penalized least squares procedure is employed for this task. Some properties of the resultant estimators are derived: the existence (and rate of convergence) of a consistent estimator for the parameters in the linear part and an oracle property for the variable selection method. Finally, a real data application illustrates the good performance of our procedure

Determining the efficacy of Xenorhabdus szentirmaii metabolites and trancinnamic acid against plant pathogenic fungus, Botrytis cinerea

YÖK Tez No: 578475Bu çalışmada Xenorhabdus szentirmaii bakteri supernatantı ile transcinnamic asit (TCA)'in bitki patojeni Botrytis cinerea fungusuna karşı etkinliği petri ve saksı deneylerinde test edilmiştir. Petri deneylerinde TCA ve bakteri supernatantı farklı oranlarda yapay besi ortamına karıştırılarak fungusun gelişimi takip edilmiştir. TCA'nın test edilen tüm konsantrasyonları (%0.5, %1 ve %2) X. szentirmaii'ye göre B. cinerea'ın misel gelişimini daha fazla inhibe etmiştir. Saksı deneylerinde ise TCA sentetik bir fungusit ile beraber B. cinerea bulaştırılmış marul fidelerine uygulanmıştır. Deney sonunda TCA sentetik fungusit kadar etkili bulunmuştur. Ancak fungusitin daha düşük dozları ile TCA'nın ikili uygulamaları arasında hiçbir sinerjitik etkileşim elde edilememiştir. Test edilen TCA ve sentetik fungusit arasındaki tüm gruplarda yalnızca antagonistik bir ilişki gözlenmiştir.In this study we evaluated the inhibitory effect of cell-free supernatant of Xenorhabdus szentirmaii and trans-cinnamic acid (TCA), against Botrytis cinerea in petri and pot experiments. In petri assays, the different concentrations of TCA and X. szentirmaii were mixtered in the media of B. cinerea and mycelial growth of fungus was followed. All tested concentration (%0.5, %1 ve %2) of TCA inhibited mycelial growth of B. cinerea better than X. szentirmaii. In pot experiments, different combinations of TCA and a synthetic fungicide were applied lettuce seedlings which infected with B. cinerea. At the end of experiments, TCA was as effective as fungicide. Whereas no synergistic interaction was detected between combined application of the lower concentrations of fungicide and TCA. Only antagonistic interaction was detected between all experiment groups