The paper suggests sufficient conditions of error-free recoverability of a missing value for sequences, i.e., discrete time processes, in the pathwise setting, without using probabilistic assumptions on the ensemble. This setting targets situations where we deal with a single sequence that is deemed to be unique and such that we cannot rely on statistics collected from other similar samples. A ...

The task of classification with incomplete data is a complex phenomena and its performance depends upon the method selected for handling the missing data. Missing data occur in datasets when no data value is stored for an attribute / feature in the dataset. This paper provides a brief overview to the problem of incomplete data handling techniques and discusses the various methods used with clas...

We consider a local post-stratification approach to analyze the capture–recapture dual system Accuracy and Coverage Evaluation (A.C.E.) data associated with the 2000 U.S. Census. The local post-stratification is carried out via a nonparametric regression estimation of the census enumeration and the correct enumeration functions. We propose a nonparametric population size estimator that is desig...

Agostinelli, Leung, Yohai, and Zamar (Agostinelli et al. in the remainder) consider the difficult problem of robust estimation based on high-dimensional data. If outlying values can appear independently in the variables, then it can easily occur that the majority of the observations in high-dimensional data are contaminated, as pointed out in Alqallaf et al. (2009). Consequently, standard robus...

Water has an outstanding importance for the life on earth. From this results the necessity for the monitoring and interpretation of marine data. For that, the visual analysis is a suitable and effective tool, whereby special demands arise from the heterogeneity of data (different data types, different data sources), the quality of data (missing values, incorrect values), and the large quantity ...

Conjoint analysis seeks to explain an ordered categorical ordinal variable according to several variables using a multiple regression scheme. A common problem encountered, there, is the presence of missing values in classification-ranks. In this paper, we are interested in the cases where consumers provide a ranking of some products instead of rating these products (i.e. explained variable pres...

In KDD procedure, to fill in missing data typically requires a very large investment of time and energy often 80% to 90% of a data analysis project is spent in making the data reliable enough so that the results can be trustful. In this paper, we propose a SVM regression based algorithm for filling in missing data, i.e. set the decision attribute (output attribute) as the condition attribute (i...

Missing data are records that are absent from a data set. They are data that were intended to be recorded, but for some reason were not. Missing values are common in data analysis and occur in almost any domain, causing problems such as biased results and reduced statistical rigour. Visual analytics has great potential to provide invaluable support for the investigation of missing data. This po...

The missing data and nonresponse problem is a usual difficulty of particular concern in medical and social science databases. Dealing with nonresponse can be a difficult matter and it is important to apply adequate missing data methods to obtain valid inference. Missing data is a very common problem in real data sets, and different methods to solve this problem have been developed. A simple and...

This paper presents a new guided image completion method which fills any missing values by considering information from a guidance image. We develop a confidence propagation scheme that allows the filling process to be carried out globally without the need of deciding the filling order. We conduct experiments in several applications where the problem can be formulated into a guided image comple...