Add to ORKGIn this note we study the conditions under which leading models for underreported counts are identified. In particular, we highlight a peculiar identification problem that afflicts two of the most popular models in this class
This thesis consists of four self-contained chapters. Chapter 2 (co-authored with Prof. Sophocles M...
In practice, outlying observations are not uncommon in many study domains. Without knowing the under...
AbstractThe traditional way to cope with missing data problems has been to combine the available dat...
Any counting system is prone to recording errors including underreporting and overreporting. Ignorin...
Underreporting in register systems can be analyzed using a binomial approach, where both the size a...
The analysis of count data within the framework of regression models plays a crucial role in many ap...
It is common for time series of unbounded counts (that is, nonnegative integers) to display overdisp...
This article describes the R package CountsEPPM and its use in determining maximum likelihood estima...
For counts it often occurs that the observed variance exceeds the nominal variance of the claimed bi...
This paper studies a class of models where full identification is not necessarily assumed. We term su...
Data quality is emerging as an essential characteristics of all data driven processes. The problem ...
We study a regression model with a binary explanatory variable that is subject to misclassification ...
Discrete data in the form of counts arise in many health science disciplines such as biology and epi...
In this thesis, some issues related with incomplete categorical data and inflated count data analyse...
This paper discusses the specification and estimation of seemingly unrelated multivariate count data...
This thesis consists of four self-contained chapters. Chapter 2 (co-authored with Prof. Sophocles M...
In practice, outlying observations are not uncommon in many study domains. Without knowing the under...
AbstractThe traditional way to cope with missing data problems has been to combine the available dat...
Any counting system is prone to recording errors including underreporting and overreporting. Ignorin...
Underreporting in register systems can be analyzed using a binomial approach, where both the size a...
The analysis of count data within the framework of regression models plays a crucial role in many ap...
It is common for time series of unbounded counts (that is, nonnegative integers) to display overdisp...
This article describes the R package CountsEPPM and its use in determining maximum likelihood estima...
For counts it often occurs that the observed variance exceeds the nominal variance of the claimed bi...
This paper studies a class of models where full identification is not necessarily assumed. We term su...
Data quality is emerging as an essential characteristics of all data driven processes. The problem ...
We study a regression model with a binary explanatory variable that is subject to misclassification ...
Discrete data in the form of counts arise in many health science disciplines such as biology and epi...
In this thesis, some issues related with incomplete categorical data and inflated count data analyse...
This paper discusses the specification and estimation of seemingly unrelated multivariate count data...
This thesis consists of four self-contained chapters. Chapter 2 (co-authored with Prof. Sophocles M...
In practice, outlying observations are not uncommon in many study domains. Without knowing the under...
AbstractThe traditional way to cope with missing data problems has been to combine the available dat...