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## Hauptgebiete der Sozialstatistik

### Statistik (1923-01-01) 442: 105-120 , January 01, 1923

## Rejoinder on: Nonparametric inference with generalized likelihood ratio tests

### TEST (2007-12-01) 16: 471-478 , December 01, 2007

## Sampling distributions under missing values

### Trabajos de estadistica y de investigacion operativa (1966-06-01) 17: 59-83 , June 01, 1966

### Resumen

En este articulo se discuten alguno de los problemas relacionados con el análisis estadistico de observaciones que faltan. En Análisis Estadistico, cuando algunas observaciones faltan se desechan las otras observaciones correspondientes a la característica bajo el estudio o tratamiento del resto de observaciones como si nada hubiera ocurrido, pero esto nos conducirá a una inferencia no científica.

En este trabajo son discutidas las distribuciones de media de una muestra y los referidos temas. Por conveniencia y simplicidad la distribución origen se supone normal y la distribución para la medida de la muestra como una distribución truncada de la Binomial o la de Poisson.

## Quantitative Methods for Current Environmental Issues

### Quantitative Methods for Current Environmental Issues (2002-01-01) , January 01, 2002

## Improved Estimators of High-Dimensional Expectation Vectors

### Multivariate Statistical Analysis (2000-01-01) 41: 112-130 , January 01, 2000

Until recently efforts to improve estimators of the expectation value vector were restricted to a special case of shrinkage estimators, that is, estimators with a scalar multiple of the sample mean [23], [25]. The distributions were assumed to be normal or centrally symmetric. In the previous chapter we considered component-wise estimators for vectors with independent components. Now we look for improved estimators of the expectation vectors for dependent variables. We start from an idea to shrink variables in a component-wise manner as in Chapter 6, but for *approximately* independent variables that are produced by passing to the system of coordinates, where the sample covariance matrix is diagonal. Thus the shrinkage coefficients will depend on the sample covariance matrix; we assume that they do not depend on any other variables including sample means.

## The Kohonen self-organizing map method: An assessment

### Journal of Classification (1995-09-01) 12: 165-190 , September 01, 1995

The “self-organizing map” method, due to Kohonen, is a well-known neural network method. It is closely related to cluster analysis (partitioning) and other methods of data analysis. In this article, we explore some of these close relationships. A number of properties of the technique are discussed. Comparisons with various methods of data analysis (principal components analysis, k-means clustering, and others) are presented.

## ASTROSCAN II: The Next Leiden Microdensitometer

### Digitised Optical Sky Surveys (1992-01-01) 174: 109-114 , January 01, 1992

In Leiden we have had the ASTROSCAN I microdensitometer operating for about fifteen years now. This machine has been described in detail by Swaans (1981, PhD thesis). As the name of the measuring machine suggests, a major part of its functionality is to accurately measure photographic densities. Although the old epoch Palomar Observatory Sky Survey (POSS) plates cannot be used for accurate photometry, many other plates, including the new epoch POSS plates, are photometrically correct. To accurately obtain photometric measures, one also needs to couple into that the positional tolerances of the measuring machine, thus obtaining high positional precision.

## New Technologies and Statistics: Partners for Environmental Monitoring and City Sensing

### Statistical Methods and Applications from a Historical Perspective (2014-01-01) , January 01, 2014

Urban space is interconnected, thanks to a vast array of technological devices whose data can be aggregated in a geographic database thereby providing a representation of what is happening around us. City Sensing is an “immersive sensing” and a new opportunity to survey the territory and the environment, by means of low-cost sensors small enough to be wearable. Advantages of such a framework are the widespread and numerous measurements at lower unit cost and also the near real-time friendly communication, together with an interaction with citizens. There are, of course, some limits: low-cost sensors’ measurements are affected by a greater error; the huge amount of data produced can result in a sort of data overload; pressure for real time can lead to hasty elaborations. Statistics can offer some help to reduce the impact of the drawbacks related to measurement quality control and error estimates, and they can also offer possible solutions for significant data synthesis and representation.

## Analysis of generalized linear mixed models via a stochastic approximation algorithm with Markov chain Monte-Carlo method

### Statistics and Computing (2002-04-01) 12: 175-183 , April 01, 2002

In recent years much effort has been devoted to maximum likelihood estimation of generalized linear mixed models. Most of the existing methods use the EM algorithm, with various techniques in handling the intractable E-step. In this paper, a new implementation of a stochastic approximation algorithm with Markov chain Monte Carlo method is investigated. The proposed algorithm is computationally straightforward and its convergence is guaranteed. A simulation and three real data sets, including the challenging salamander data, are used to illustrate the procedure and to compare it with some existing methods. The results indicate that the proposed algorithm is an attractive alternative for problems with a large number of random effects or with high dimensional intractable integrals in the likelihood function.

## Statistical Inference Focusing on a Single Mean

### Foundations of Applied Statistical Methods (2014-01-01): 31-62 , January 01, 2014

Statistical inference is to infer whether or not the observed sample data are evidencing the population characteristics of interest. If the whole population data were gathered collectively then there is no room for uncertainty about the population due to a sampling and the statistical inference is unnecessary. It is ideal but unrealistic to collect the whole population data and complete the investigation solely by descriptive data analysis. For this reason, a smaller size of sample data set than that of the whole population is gathered for an investigation. Since the sample data set does not populate the entire population, it is not identical to the population. This chapter will discuss the relationship between the population and sample by addressing (1) the uncertainty and errors in the sample, (2) underpinnings that are necessary for a sound understanding of the applied methods of statistical inference, (3) forms and paradigms of drawing inference, and (4) good study design as a solution to minimize the unavoidable errors contained in the sampling.