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## Front Matter - The SPSS Guide to the New Statistical Analysis of Data

### The SPSS Guide to the New Statistical Analysis of Data (1997-01-01) , January 01, 1997

## Matrix Algebra for MDS

### Modern Multidimensional Scaling (1997-01-01): 109-134 , January 01, 1997

In this chapter, we build a basis for a more technical understanding of MDS. Matrices are of particular importance here. They bring together, in one single mathematical object, such notions as a whole configuration of points, all of the distances among the points of this configuration, or a complete matrix of proximities. Mathematicians developed a sophisticated algebra for matrices that allows one to derive, for example, how a configuration that represents a matrix of distances can be computed, or how the distances among all points can be derived from a configuration. Most of these operations can be written in just a few lines, in very compact notation, which helps tremendously to see what is going on. The reader does not have to know everything in this chapter to read on in this book. It suffices to know the main concepts and theorems and then later come back to this chapter when necessary. Proofs in this chapter are meant to better familiarize the reader with the various notions. One may opt to skip the proofs and accept the respective theorems, as is common practice in mathematics (“It can be shown that...”).

## The multiresolution histogram

### Metrika (1997-01-01) 46: 41-57 , January 01, 1997

We introduce a new method for locally adaptive histogram construction that doesn’t resort to a standard distribution and is easy to implement: the multiresolution histogram. It is based on a*L*_{2} analysis of the mean integrated squared error with Haar wavelets and hence can be associated with a multiresolution analysis of the sample space.

## Observations of Millisecond Pulsars on Time Scales from 10 Nanoseconds to 10 Years

### Astronomical Time Series (1997-01-01) 218: 25-36 , January 01, 1997

At this conference devoted to the study of a variety of astrophysical time series, it is fitting to note that 1997 marks the 30th anniversary year of the discovery of pulsars, which produce arguably the most famous astronomical time series (Hewish *et al*., 1968). The diversity and richness of science that has developed from observations of pulsars could never have been foreseen by their discoverers. Most of this science is deduced from detailed studies of pulsar time series. Timing observations of millisecond pulsars can be made with precisions that are unparalleled in astronomy. In this review, following a brief introduction to the pulsar population in general and to millisecond pulsars in particular, I will describe timing observations, highlight results of long-term high-precision timing observations of millisecond pulsars, and finally, present a look to the future in this active field.

## Front Matter - Intelligence, Genes, and Success

### Intelligence, Genes, and Success (1997-01-01) , January 01, 1997

## Analysis of Mean and Rate Functions for Recurrent Events

### Proceedings of the First Seattle Symposium in Biostatistics (1997-01-01) 123: 37-49 , January 01, 1997

Methods for the analysis of mean and rate functions for recurrent events are reviewed, in the context of longitudinal studies involving multiple subjects. Semiparametric models involving an arbitrary baseline mean or rate function multiplied by a parametrically specified function of covariates are emphasized. Methods of dealing with censoring or terminal events which are related to the recurrent events are considered. An example involving rejection episodes for kidney transplant recipients is discussed.

## On the Asymptotic Expectations of Some Unit Root Tests in a First Order Autoregressive Process in the Presence of Trend

### Annals of the Institute of Statistical Mathematics (1997-09-01) 49: 585-599 , September 01, 1997

Estimation in a first order autoregressive process with trend isconsidered. Integral expressions for the asymptotic bias of the estimatorunder a unit root and for the expectation of the limit distribution of thelog likelihood ratio test for a unit root are given, and evaluatednumerically.

## Answering Questions About Population Characteristics

### The SPSS Guide to the New Statistical Analysis of Data (1997-01-01): 117-131 , January 01, 1997

This chapter describes how to use SPSS to test hypotheses about characteristics of a single population. SPSS for Windows has a procedure that computes the test statistic for a one-sample test only when the population standard deviation (σ) is unknown. When σ is known, you must compute the test statistic by hand, but you can use SPSS to find the sample mean, proportion, or median. This is especially useful when sample sizes are large and manual calculations are cumbersome.

## Identification of latent class Markov models with multiple indicators and correlated measurement errors

### Journal of the Italian Statistical Society (1997-12-01) 6: 201-211 , December 01, 1997

### Summary

A necessary condition for identification of latent class models is that the number of unknown independent parameters must not be greater than the number of observed cells in the contingency table. Such condition is not sufficient at all. Verifying Goodman’s sufficient condition for local identifiability may be, for complex models, a cumbersome procedure. In any case, local identifiability does not guarantee global identifability. The paper provides rules to ascertain global identifiability of some specifications of latent class Markov models, expressing the unknown parameters as a function of the observed frequencies. In the case that not all parameters of a model are identified, the outlined rules provide hints about the restrictions to impose in order to obtain fully identified models.