Search results for "model selection"
showing 4 items of 64 documents
Model selection procedure for mixture hidden Markov models
2021
This paper proposes a model selection procedure to identify the number of clusters and hidden states in discrete Mixture Hidden Markov models (MHMMs). The model selection is based on a step-wise approach that uses, as score, information criteria and an entropy criterion. By means of a simulation study, we show that our procedure performs better than classical model selection methods in identifying the correct number of clusters and hidden states or an approximation of them
Clickstream Data Analysis: A Clustering Approach Based on Mixture Hidden Markov Models
2023
Nowadays, the availability of devices such as laptops and cell phones enables one to browse the web at any time and place. As a consequence, a company needs to have a website so as to maintain or increase customer loyalty and reach potential new customers. Besides, acting as a virtual point-of-sale, the company portal allows it to obtain insights on potential customers through clickstream data, web generated data that track users accesses and activities in websites. However, these data are not easy to handle as they are complex, unstructured and limited by lack of clear information about user intentions and goals. Clickstream data analysis is a suitable tool for managing the complexity of t…
On overall sampling plan for small area estimation
2017
The time and budget restrictions in survey sampling can impose limits on the area sample sizes. This may reduce the possibility to obtain area-specific and population parameters estimates with adequate precision. Market research companies and institutes for producing official statistics face frequently this problem. Various models and methods for small area estimation (SAE) have been developed to solve this problem. The sample allocation must support the selected model and method to ensure efficient estimation and must be implemented in the design phase of the survey. The proposed allocation is developed by incorporating auxiliary information, a model, and an estimation method. The estimate…
Model selection using limiting distributions of second-order blind source separation algorithms
2015
Signals, recorded over time, are often observed as mixtures of multiple source signals. To extract relevant information from such measurements one needs to determine the mixing coefficients. In case of weakly stationary time series with uncorrelated source signals, this separation can be achieved by jointly diagonalizing sample autocovariances at different lags, and several algorithms address this task. Often the mixing estimates contain close-to-zero entries and one wants to decide whether the corresponding source signals have a relevant impact on the observations or not. To address this question of model selection we consider the recently published second-order blind identification proced…