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## Sequential Monte Carlo for counting vertex covers in general graphs

### Statistics and Computing (2016-05-01) 26: 591-607 , May 01, 2016

In this paper we describe a sequential importance sampling (SIS) procedure for counting the number of vertex covers in general graphs. The optimal SIS proposal distribution is the uniform over a suitably restricted set, but is not implementable. We will consider two proposal distributions as approximations to the optimal. Both proposals are based on randomization techniques. The first randomization is the classic probability model of random graphs, and in fact, the resulting SIS algorithm shows polynomial complexity for random graphs. The second randomization introduces a probabilistic relaxation technique that uses Dynamic Programming. The numerical experiments show that the resulting SIS algorithm enjoys excellent practical performance in comparison with existing methods. In particular the method is compared with *cachet*—an exact model counter, and the state of the art *SampleSearch*, which is based on Belief Networks and importance sampling.

## Multivariate Time Series

### Introduction to Time Series and Forecasting (2016-01-01): 227-257 , January 01, 2016

Many time series arising in practice are best considered as components of some vector- valued (multivariate) time series {*X*_{t}} having not only serial dependence within each component series {*X*_{ti}} but also interdependence between the different component series {*X*_{ti}} and {*X*_{tj}}, *i* ≠ *j*. Much of the theory of univariate time series extends in a natural way to the multivariate case; however, new problems arise.

## Front Matter - Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics

### Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics (2016-01-01) , January 01, 2016

## Textual Information Localization and Retrieval in Document Images Based on Quadtree Decomposition

### Analysis of Large and Complex Data (2016-01-01): 71-78 , January 01, 2016

Textual information extraction is a challenging issue in Information Retrieval. Two main approaches are commonly distinguished: texture-based and region-based. In this paper, we propose a method guided by the quadtree Quadtree decomposition. The principle of the method is to recursively decompose regions of a document image is four equal regions, starting from the image of the whole document. At each step of the decomposition process an OCR engine is used for retrieving a given textual information from the obtained regions. Experiments on real invoice data provide promising results.

## Goodness-of-fit tests for semiparametric and parametric hypotheses based on the probability weighted empirical characteristic function

### Statistical Papers (2016-12-01) 57: 957-976 , December 01, 2016

We investigate the finite-sample properties of certain procedures which employ the novel notion of the probability weighted empirical characteristic function. The procedures considered are: (1) Testing for symmetry in regression, (2) Testing for multivariate normality with independent observations, and (3) Testing for multivariate normality of random effects in mixed models. Along with the new tests alternative methods based on the ordinary empirical characteristic function as well as other more well known procedures are implemented for the purpose of comparison.

## Robust estimation of generalized partially linear model for longitudinal data with dropouts

### Annals of the Institute of Statistical Mathematics (2016-10-01) 68: 977-1000 , October 01, 2016

In this paper, we study the robust estimation of generalized partially linear models (GPLMs) for longitudinal data with dropouts. We aim at achieving robustness against outliers. To this end, a weighted likelihood method is first proposed to obtain the robust estimation of the parameters involved in the dropout model for describing the missing process. Then, a robust inverse probability-weighted generalized estimating equation is developed to achieve robust estimation of the mean model. To approximate the nonparametric function in the GPLM, a regression spline smoothing method is adopted which can linearize the nonparametric function such that statistical inference can be conducted operationally as if a generalized linear model was used. The asymptotic properties of the proposed estimator are established under some regularity conditions, and simulation studies show the robustness of the proposed estimator. In the end, the proposed method is applied to analyze a real data set.

## M-Quantile Small Area Estimation for Panel Data

### Topics in Theoretical and Applied Statistics (2016-01-01) , January 01, 2016

Economic indicators need to be estimated at regional level. Small area estimation based on M-quantile regression has recently been introduced by Chambers and Tzavidis (Biometrika 93:255–268, 2006) and it has proved to provide a valid alternative to traditional methods. Thus far, this method has only been applied to cross-sectional data. However, it is well known that the use of panel data may provide significant gains in terms of efficiency of the estimators. This paper explores possible extensions of M-quantile-based small area estimators to the panel data context. A model-based simulation study is presented.

## R als Programmiersprache

### R kompakt (2016-01-01): 247-255 , January 01, 2016

### Zusammenfassung

R bietet nicht nur Mittel zur numerischen und grafischen Datenanalyse, sondern ist gleichzeitig eine Programmiersprache, die dieselbe Syntax wie statistische Auswertungen verwendet. Das sehr umfangreiche Thema der Programmierung mit R wird in den folgenden Abschnitten soweit angedeutet, dass nützliche Sprachkonstrukte wie Fallunterscheidungen und Schleifen verwendet sowie einfache Funktionen selbst erstellt werden können. Das Kapitel schließt mit Möglichkeiten, wie sich die Effizienz von Auswertungen steigern lässt.

## Two-phase outcome-dependent studies for failure times and testing for effects of expensive covariates

### Lifetime Data Analysis (2016-11-29): 1-17 , November 29, 2016

Two- or multi-phase study designs are often used in settings involving failure times. In most studies, whether or not certain covariates are measured on an individual depends on their failure time and status. For example, when failures are rare, case–cohort or case–control designs are used to increase the number of failures relative to a random sample of the same size. Another scenario is where certain covariates are expensive to measure, so they are obtained only for selected individuals in a cohort. This paper considers such situations and focuses on cases where we wish to test hypotheses of no association between failure time and expensive covariates. Efficient score tests based on maximum likelihood are developed and shown to have a simple form for a wide class of models and sampling designs. Some numerical comparisons of study designs are presented.