Kernel-based methods for combining information of several frame surveys

A sample selected from a single sampling frame may not represent adequatly the entire population. Multiple frame surveys are becoming increasingly used and popular among statistical agencies and private organizations, in particular in situations where several sampling frames may provide better coverage or can reduce sampling costs for estimating population quantities of interest. Auxiliary information available at the population level is often categorical in nature, so that incorporating categorical and continuous information can improve the efficiency of the method of estimation. Nonparametric regression methods represent a widely used and flexible estimation approach in the survey context. We propose a kernel regression estimator for dual frame surveys that can handle both continuous and categorical data. This methodology is extended to multiple frame surveys. We derive theoretical properties of the proposed methods and numerical experiments indicate that the proposed estimator perform well in practical settings under different scenarios.Ministerio de Economía y CompetitividadConsejería de Economía, Innovación, Ciencia y Emple

On the size and power of testing for no autocorrelation under weak assumptions

Recently, Lobato (Journal of the American Statistical Association, 96, 1066-76, 2001) proposed a robust test of no autocorrelation on a time series when the series is possibly dependent. While the Lobato test is shown to be accurate in size, its power performance is unsatisfactory. This paper seeks to improve the power of the Lobato test without comprising its good size property. Based on the recent works of Jansson (2004) and Phillips et al. (2003), two classes of modified Lobato tests are suggested. It is found that the Lobato test and its Phillips-Sun-Jin modification exhibit very similar control over size while the Jansson modification tends to be more vulnerable to size distortion. It is also found that both modified tests dominate the Lobato test in terms of local asymptotic power and in terms of finite sample power and the Phillips-Sun-Jin modification seems to outperform the Jansson modification. Autocorrelations in monthly financial asset (stock/bond) returns are investigated.

Kernel methods for detecting the direction of time series

Summary. We propose two kernel based methods for detecting the time direction in empirical time series. First we apply a Support Vector Machine on the finitedimensional distributions of the time series (classification method) by embedding these distributions into a Reproducing Kernel Hilbert Space. For the ARMA method we fit the observed data with an autoregressive moving average process and test whether the regression residuals are statistically independent of the past values. Whenever the dependence in one direction is significantly weaker than in the other we infer the former to be the true one. Both approaches were able to detect the direction of the true generating model for simulated data sets. We also applied our tests to a large number of real world time series. The ARMA method made a decision for a significant fraction of them, in which it was mostly correct, while the classification method did not perform as well, but still exceeded chance level

A Mixed Model-assisted Regression Estimator that Uses Variables Employed at the Design Stage

Finite Population, Auxiliary Information, Design based approach, Optimal estimator, REML,

On kernel nonparametric regression designed for complex survey data

Bandwidth, Design-based inference, Local linear regression, Local polynomial regression, Model-based inference, Nonparametric regression, Sampling weights, Survey sampling,