Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data, but due to an assumed local independence structure there may not be a correspon...

This work considers a problem of integrating heterogeneous semi–structured data sources with the purpose of estimating integration quality (IQ). During the integration of such data sources the IQ estimation plays an important role, because correspondences and dependencies within and across the sources are not completely known, the schema or semantics might be missing, which leads to results wit...

This article examines the problem of specification error in 2 models for categorical latent variables; the latent class model and the latent Markov model. Specification error in the latent class model focuses on the impact of incorrectly specifying the number of latent classes of the categorical latent variable on measures of model adequacy as well as sample reallocation to latent classes. The ...

A spatial latent class analysis model that extends the classic latent class analysis model by adding spatial structure to the latent class distribution through the use of the multinomial probit model is introduced. Linear combinations of independent Gaussian spatial processes are used to develop multivariate spatial processes that are underlying the categorical latent classes. This allows the l...

We inspect stroke sets from CEDAR digits in LCM UpWrite space.

Previous research by Tucker et al. (2010), working with the Consumer Expenditure Survey (CE), explores the factor structure of measurement error indicators such as: interview length, extent and type of records used, the monthly patterns of reporting, reporting of income, attempt history information, and response behavior across multiple interviews in a latent class model. Findings from this res...

In this paper three problems posed in [1J and concerning the Smarandache LeM sequence have been analysed. Introduction In [1] the Smarandache LCM . sequence is defined as the least common mUltiple (LCM) of (1,2,3, .,. ,n) : 1,2,6, 12,60,60,420,840,2520,2520,27720,27720,360360, 360360, 360360, 720720 ...... . In the same paper the following three problems are reported: 1. If a(n) is the n-th ter...

For any positive integer n, the famous F.Smarandache LCM function SL(n) is defined as the smallest positive integer k such that n | [1, 2, · · · , k], where [1, 2, · · · , k] denotes the least common multiple of 1, 2, · · · , k. The main purpose of this paper is using the elementary methods to study the mean value properties of ln SL(n), and give a sharper asymptotic formula for it.

The purpose of the present study was to compare the results of Configural Frequency Analysis (CFA) and solutions derived from Latent Class Analysis (LCA) to evaluate concordances and disconcordances regarding their outcomes. Explorative LCA was applied to the Big Five dataset described in Lautsch & Thöle (this issue). The comparative analyses of the LCA solutions and the CFA results demonstrate...

Typically, marketers define market segments by their demographic characteristics, assuming that these segments represent consumers with relatively homogeneous buying patterns. A more managerially useful definition, however, groups consumers of similar behavior directly and then seeks to find demographic comrnonalities among them. This study uses a latent class analysis technique to segment cons...