Retrospective change detection in categorical time series

The aim of this paper is to propose methods of detecting change in the coefficients of a multinomial logistic regression model for categorical time series offline. The alternatives to the null hypothesis of stationarity can be either the hypothesis that it is not true, or that there is a temporary change in the sequence. We use the efficient score vector of the partial likelihood function. This has several advantages. First, the alternative value of the parameter does not have to be estimated; hence, we have a procedure that has a simple structure with only one parameter estimation using all available observations. This is in contrast with the generalized likelihood ratio-based change point tests. The efficient score vector is used in various ways. As a vector, its components correspond to the different components of the multinomial logistic regression model’s parameter vector. Using its quadratic form a test can be defined, where the presence of a change in any or all parameters is tested for. If there are too many parameters one can test for any subset while treating the rest as nuisance parameters. Our motivating example is a DNA sequence of four categories, and our test result shows that in the published data the distribution of the four categories is not stationary.     

The impact of internal communication on strategic and economic effects in Serbian companies

This paper presents the research results in relation to examining and defining dependences and the impact of internal communication on certain strategic and economic business effects. Serbian companies were the focus of this research. The data were collected by polling 100 managers from 100 companies. Afterwards, multivariate regression and correlation was carried out in the aim of the final determination of requested dependences. This research confirmed a strong positive relation between internal communication and strategic and economic business effects.     

The weighted sequential likelihood ratio

Strong approximation of the maximum-likelihood-ratio statistic by a diffusion process is given. This allows one to develop statistics using different weight functions. Sequential tests obtained include the ones earlier defined by Barbour (1979). The precision of the approximations is examined.     

Sequential Comparison of d Populations and Related Tables

The paper deals with the numerical solution of the likelihood equations for incomplete data from exponential families, that is for data being a function of exponential family data. Illustrative examples especially studied in this paper concern grouped and censored normal samples and normal mixtures. A simple iterative method of solution is proposed and studied. It is shown that the sequence of iterates converges to a relative maximum of the likelihood function, and that the convergence is geometric with a factor of convergence which for large samples equals the maxi-mal relative loss of Fisher information due to the incompleteness of data. This large sample factor of convergence is illustrated diagrammaticaily for the examples mentioned above. Experiences of practical application are mentioned.     

A class of sequential tests for two‐sample composite hypotheses

The authors propose a class of statistics based on Rao's score for the sequential testing of composite hypotheses comparing two treatments (populations). Asymptotic approximations of the statistics lead them to propose sequential tests and to derive their monitoring boundaries. As special cases, they construct sequential versions of the two‐sample t‐test for normal populations and two‐sample z‐score tests for binomial populations. The proposed algorithms are simple and easy to compute, as no numerical integration is required. Furthermore, the user can analyze the data at any time regardless of how many inspections have been made. Monte Carlo simulations allow the authors to compare the power and the average stopping time (also known as average sample number) of the proposed tests to those of nonsequential and group sequential tests. A two‐armed comparative clinical trial in patients with adult leukemia allows them to illustrate the efficiency of their methods in the case of binary responses.     

Attitudes to variation in spoken Faroese

This paper presents results from a language attitude test in the Faroe Islands, a small country with considerable dialectal variation and no standard spoken language. The test used is a replica of the verbal guise test method that has been used through the SLICE research network, which includes a verbal guise test to elicit reactions to five regional varieties of Faroese and a label ranking task to elicit statements on language variation. There were 203 Faroese 15‐year‐old students tested. The results from the label ranking task indicate a common notion of a national Faroese dialect hierarchy that favors that of the capital. However, the reaction patterns in the verbal guise test reflect no such hierarchy. The Faroese results thus diverge from the evaluation patterns found in a number of comparable European verbal guise tests, yet are similar to the patterns found in one European test locality: Western Norway.     

The likelihood ratio under noncontiguous alternatives

The asymptotic distribution of the likelihood ratio under noncontiguous alternatives is shown to be normal for the exponential family of distributions. The rate of convergence of the parameters to the hypothetical value is specified where the asymptotic noncentral chi-square distribution no longer holds. It is only a little slower than $\O\left( {n^{ - \frac{1}{2}} } \right)$. The result provides compact power approximation formulae and is shown to work reasonably well even for moderate sample sizes.     

Testing for changes in the covariance structure of linear processes

We consider several procedures to detect changes in the mean or the covariance structure of a linear process. The tests are based on the weighted CUSUM process. The limit distributions of the test statistics are derived under the no change null hypothesis. We develop new strong and weak approximations for the sample mean as well as the sample correlations of linear processes. A small Monte Carlo simulation illustrates the applicability of our results.