Add to ORKGAlthough models for time series of counts are currently intensively studied by a number of researchers, analysis on how integer-valued time series with exhibited seasonality can be modelled is still limited. This thesis is focused on formulating models for bivariate integer-valued time series with exhibited seasonality. Hence, three models suitable to such are formulated: BINAR(1), BINGARCH and TV-BINAR(1). The considered estimation methods are tested on the simulated data. All three models are applied on the bivariate car accident data. Time series consist of number of car accidents caused by the alcohol intoxicated drivers and a number of car accidents caused by the sober drivers in Lithuania per month
Traditional crash count models, such as the Poisson and Negative Binomial models, do not account for...
In the present paper a bivariate integer-valued autoregressive process of order 1 (BINAR(1)) and an ...
textabstractA recurring issue in modeling seasonal time series variables is the choice of the most a...
Integer-valued time series comprising count observations at regular time intervals can be observed i...
Thesis is focused on a time series analysis of uninsured vehicles accidents in Czech republic betwee...
Integer-valued time series comprising count observations at regular time intervals can be observed i...
The aim of this work is to assess the modeling performance of two bivariate models for time series o...
This thesis deals with INGARCH models for a count time series. Main emphasis is placed on a linear I...
The diploma thesis deals with the modelling of seasonal time series and its illustrations on a real ...
Non–negative integer–valued time series are often encountered in many different scientific fields, u...
To achieve greater sustainability of the traffic system, the trend of traffic accidents in road traf...
This bachelor thesis deals with the time series of binary variables that exist in many social sphere...
In this Master Thesis there are summarized basic methods for modelling time series, such as linear r...
Time series data are sometimes affected by multiple cycles of different lengths. There can be a week...
Motivated by a large dataset containing time series of weekly number of rainy days collected over tw...
Traditional crash count models, such as the Poisson and Negative Binomial models, do not account for...
In the present paper a bivariate integer-valued autoregressive process of order 1 (BINAR(1)) and an ...
textabstractA recurring issue in modeling seasonal time series variables is the choice of the most a...
Integer-valued time series comprising count observations at regular time intervals can be observed i...
Thesis is focused on a time series analysis of uninsured vehicles accidents in Czech republic betwee...
Integer-valued time series comprising count observations at regular time intervals can be observed i...
The aim of this work is to assess the modeling performance of two bivariate models for time series o...
This thesis deals with INGARCH models for a count time series. Main emphasis is placed on a linear I...
The diploma thesis deals with the modelling of seasonal time series and its illustrations on a real ...
Non–negative integer–valued time series are often encountered in many different scientific fields, u...
To achieve greater sustainability of the traffic system, the trend of traffic accidents in road traf...
This bachelor thesis deals with the time series of binary variables that exist in many social sphere...
In this Master Thesis there are summarized basic methods for modelling time series, such as linear r...
Time series data are sometimes affected by multiple cycles of different lengths. There can be a week...
Motivated by a large dataset containing time series of weekly number of rainy days collected over tw...
Traditional crash count models, such as the Poisson and Negative Binomial models, do not account for...
In the present paper a bivariate integer-valued autoregressive process of order 1 (BINAR(1)) and an ...
textabstractA recurring issue in modeling seasonal time series variables is the choice of the most a...