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Application of the Cox regression model with time dependent parameters to unemployment data
Volf, Petr
2019 - English
The contribution deals with the application of statistical survival analysis with the intensity described by a generalized version of Cox regression model with time dependent parameters. A\nmethod of model components non-parametric estimation is recalled, the flexibility of result is assessed with a goodness-of-fit test based on martingale residuals. The application\nconcerns to the real data representing the job opportunities development and reduction, during a given period. The risk of leaving the company is changing in time and depends also on the age of employees and their time with company. Both these covariates are considered and their impact to the risk analyzed.
Keywords:
mathematical statistics; survival analysis; unemployment data
Fulltext is available at external website.
Application of the Cox regression model with time dependent parameters to unemployment data
The contribution deals with the application of statistical survival analysis with the intensity described by a generalized version of Cox regression model with time dependent parameters. A\nmethod of ...
A Robustified Metalearning Procedure for Regression Estimators
Kalina, Jan; Neoral, A.
2019 - English
Metalearning represents a useful methodology for selecting and recommending a suitable algorithm or method for a new dataset exploiting a database of training datasets. While metalearning is potentially beneficial for the analysis of economic data, we must be aware of its instability and sensitivity to outlying measurements (outliers) as well as measurement errors. The aim of this paper is to robustify the metalearning process. First, we prepare some useful theoretical tools exploiting the idea of implicit weighting, inspired by the least weighted squares estimator. These include a robust coefficient of determination, a robust version of mean square error, and a simple rule for outlier detection in linear regression. We perform a metalearning study for recommending the best linear regression estimator for a new dataset (not included in the training database). The prediction of the optimal estimator is learned over a set of 20 real datasets with economic motivation, while the least squares are compared with several (highly) robust estimators. We investigate the effect of variable selection on the metalearning results. If the training as well as validation data are considered after a proper robust variable selection, the metalearning performance is improved remarkably, especially if a robust prediction error is used.
Keywords:
model choice; computational statistics; robustness; variable selection
Fulltext is available at external website.
A Robustified Metalearning Procedure for Regression Estimators
Metalearning represents a useful methodology for selecting and recommending a suitable algorithm or method for a new dataset exploiting a database of training datasets. While metalearning is ...
Two Algorithms for Risk-averse Reformulation of Multi-stage Stochastic Programming Problems
Šmíd, Martin; Kozmík, Václav
2018 - English
Many real-life applications lead to risk-averse multi-stage stochastic problems, therefore effective solution of these problems is of great importance. Many tools can be used to their solution (GAMS, Coin-OR, APML or, for smaller problems, Excel), it is, however, mostly up to researcher to reformulate the problem into its deterministic equivalent. Moreover, such solutions are usually one-time, not easy to modify for different applications. We overcome these problems by providing a front-end software package, written in C++, which enables to enter problem definitions in a way close to their mathematical definition. Creating of a deterministic equivalent (and its solution) is up to the computer. In particular, our code is able to solve linear multi-stage with Multi-period Mean-CVaR or Nested Mean-CVaR criteria. In the present paper, we describe the algorithms, transforming these problems into their deterministic equivalents.
Keywords:
Multi-stage stochastic programming; deterministic equivalent; multi-period CVaR; nested CVaR; optimization algorithm
Fulltext is available at external website.
Two Algorithms for Risk-averse Reformulation of Multi-stage Stochastic Programming Problems
Many real-life applications lead to risk-averse multi-stage stochastic problems, therefore effective solution of these problems is of great importance. Many tools can be used to their solution (GAMS, ...
Problem of competing risks with covariates: Application to an unemployment study
Volf, Petr
2018 - English
The study deals with the methods of statistical analysis in the situation of competing risks in the presence of regression. First, the problem of identification of marginal and joint distributions of competing random variables is recalled. The main objective is then to demonstrate that the parameters and, in particular, the correlation of competing variables, may depend on covariates. The approach is applied to solution of a real example with unemployment data. The model uses the Gauss copula and Cox’s regression model.
Keywords:
statistical analysis; competing risks; unemployment study
Fulltext is available at external website.
Problem of competing risks with covariates: Application to an unemployment study
The study deals with the methods of statistical analysis in the situation of competing risks in the presence of regression. First, the problem of identification of marginal and joint distributions of ...
Heuristics in blind source separation
Kautský, Václav; Štěch, Jakub
2018 - English
This paper deals with application of heuristic algorithms (DEBR, MCRS) in blind source separation (BSS). BSS methods focus on a separation of the (source) signal from a linear mixture. The idea of using heuristic algorithms is introduced on the independent component extraction (ICE) model. The motivation for considering heuristics is to obtain an initial guess needed by many ICE algorithms. Moreover, the comparison of this initialization, and other algorithms accuracy is performed.\n
Keywords:
Blind Source Separation; DEBR; Independent Component Extraction
Fulltext is available at external website.
Heuristics in blind source separation
This paper deals with application of heuristic algorithms (DEBR, MCRS) in blind source separation (BSS). BSS methods focus on a separation of the (source) signal from a linear mixture. The idea of ...
Validation of comprehensive energy management system based on cloud-sourced information
Nedoma, P.; Herda, Z.; Franc, Z.; Plíhal, Jiří
2018 - English
The main research activity was devoted to develop an application that would enable testing interface between OIKOS board (based on the AURIXTM) and the dissemination module represented by Skoda vehicle demonstrator through serial port RS232. The testing was based on sending the GPS coordinates to the OIKOS unit and receiving recommended speed profile for the given track. While dissemination unit has received GPS coordinates, AURIX chip has sent back messages with prediction of possible speed profile. Further tasks included verification other forms of transmission, such as Wi-Fi, Bluetooth, Ethernet.\n\n
Keywords:
OIKOS board; dissemination module; GPS
Fulltext is available at external website.
Validation of comprehensive energy management system based on cloud-sourced information
The main research activity was devoted to develop an application that would enable testing interface between OIKOS board (based on the AURIXTM) and the dissemination module represented by Skoda ...
Experiment: Cooperative Decision Making via Reinforcement Learning
Berka, Milan
2018 - English
This report inspects cooperative decision making task using reinforcement learning. It serves for comparison with methodology based on fully probabilistic design of decision strategies.
Keywords:
decision making; reinforcement learning; cooperation
Fulltext is available at external website.
Experiment: Cooperative Decision Making via Reinforcement Learning
This report inspects cooperative decision making task using reinforcement learning. It serves for comparison with methodology based on fully probabilistic design of decision strategies.
Balancing Exploitation and Exploration via Fully Probabilistic Design of Decision Policies
Kárný, Miroslav; Hůla, František
2018 - English
Adaptive decision making learns an environment model serving a design of a decision policy. The policy-generated actions influence both the acquired reward and the future knowledge. The optimal policy properly balances exploitation with exploration. The inherent dimensionality\ncurse of decision making under incomplete knowledge prevents the realisation of the optimal design.
Keywords:
Exploitation; Exploration; Bayesian estimation; Adaptive systems; Fully probabilistic design; Kullback-Leibler divergence; Decision policy; Markov decision process
Fulltext is available at external website.
Balancing Exploitation and Exploration via Fully Probabilistic Design of Decision Policies
Adaptive decision making learns an environment model serving a design of a decision policy. The policy-generated actions influence both the acquired reward and the future knowledge. The optimal policy ...
Dynamic Bayesian Networks for the Classification of Sleep Stages
Vomlel, Jiří; Kratochvíl, Václav
2018 - English
Human sleep is traditionally classified into five (or six) stages. The manual classification is time consuming since it requires knowledge of an extensive set of rules from manuals and experienced experts. Therefore automatic classification methods appear useful for this task. In this paper we extend the approach based on Hidden Markov Models by relating certain features not only to the current time slice but also to the previous one. Dynamic Bayesian Networks that results from this generalization are thus capable of modeling features related to state transitions. Experiments on real data revealed that in this way we are able to increase the prediction accuracy.
Keywords:
Dynamic Bayesian Network; Sleep Analysis
Fulltext is available at external website.
Dynamic Bayesian Networks for the Classification of Sleep Stages
Human sleep is traditionally classified into five (or six) stages. The manual classification is time consuming since it requires knowledge of an extensive set of rules from manuals and experienced ...
DCTOOL-A4
Bakule, Lubomír; Papík, Martin; Rehák, Branislav
2018 - English
DCTOOL-A4 report presents draft of a manuscript, which is intended to be submitted for publication. The report provides a novel systematic approach to the analysis of asymptotic stability for output event-triggered uncertain centralized control systems. A class of nonlinear but nominally linear systems possessing unknown time-varying bounded uncertainties with known bounds is considered. Uncertainties are allowed in all system matrices. Original LMI-based suffi cient conditions are derived to guarantee asymptotic stability of closed-loop systems with both static output and observer-based feedback loop under even-triggered control. Both these output feedback strategies are extended to model-based uncertain control systems with\nquantized measurements. A logarithmic quantizer is considered. The Lyapunov-based approach and convex optimization serve as the main methods to derive the asymptotic LMI-based stability conditions. Bounds on the inter-event times to avoid the Zeno-effect are proved for all the cases considered. Finally, feasibility and effi ciency of the proposed strategies is demonstrated by providing numerical examples.
Keywords:
event-triggered control; networked control systems; large scale complex systems
Available at various institutes of the ASCR
DCTOOL-A4
DCTOOL-A4 report presents draft of a manuscript, which is intended to be submitted for publication. The report provides a novel systematic approach to the analysis of asymptotic stability for output ...
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