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      <div class="TdmEntry">Overall Objectives<ul><li><a href="./uid3.html">Presentation</a></li><li><a href="./uid7.html">Highlights of the Year</a></li></ul></div>
      <div class="TdmEntry">Research Program<ul><li class="tdmActPage"><a href="uid9.html&#10;&#9;&#9;  ">Introduction</a></li><li><a href="uid10.html&#10;&#9;&#9;  ">Main research topics</a></li></ul></div>
      <div class="TdmEntry">Application Domains<ul><li><a href="uid18.html&#10;&#9;&#9;  ">Dependability and safety</a></li></ul></div>
      <div class="TdmEntry">Software and Platforms<ul><li><a href="uid20.html&#10;&#9;&#9;  ">Package edrGraphicalTools</a></li><li><a href="uid21.html&#10;&#9;&#9;  ">Package ClustOfVar</a></li><li><a href="uid22.html&#10;&#9;&#9;  ">Package PCAmixdata</a></li></ul></div>
      <div class="TdmEntry">New Results<ul><li><a href="uid24.html&#10;&#9;&#9;  ">Nonparametric estimation of the jump rate for non-homogeneous marked renewal processes.</a></li><li><a href="uid25.html&#10;&#9;&#9;  ">Nonparametric estimation of the conditional distribution of the inter-jumping times for piecewise-deterministic markov processes</a></li><li><a href="uid26.html&#10;&#9;&#9;  ">Finite Linear Programming Approximations of constrained discounted Markov decision processes</a></li><li><a href="uid27.html&#10;&#9;&#9;  ">Stochastic Approximations of Constrained Discounted Markov Decision Processes</a></li><li><a href="uid28.html&#10;&#9;&#9;  ">The expected total cost criterion for Markov decision processes under constraints</a></li><li><a href="uid29.html&#10;&#9;&#9;  ">Optimal stopping for piecewise-deterministic Markov processes and applications</a></li><li><a href="uid30.html&#10;&#9;&#9;  ">Stochastic control for underwater optimal trajectories</a></li><li><a href="uid31.html&#10;&#9;&#9;  ">Modeling of cell division data</a></li><li><a href="uid32.html&#10;&#9;&#9;  ">Numerical method for the filtering of Markov jump linear systems</a></li><li><a href="uid33.html&#10;&#9;&#9;  ">Optimization of the assembly line of the future European launcher</a></li><li><a href="uid34.html&#10;&#9;&#9;  ">A variable clustering approach for the typology of units: a survey on farming and environment</a></li><li><a href="uid35.html&#10;&#9;&#9;  ">Multiple Facctorial Analysis for mixed data type</a></li><li><a href="uid36.html&#10;&#9;&#9;  ">Detecting mental states of alertness with genetic algorithm variable selection</a></li><li><a href="uid37.html&#10;&#9;&#9;  ">ClustOfVar : an R package for dimension reduction via clustering of variables. Application in supervised classification and variable selection in gene expressions data</a></li><li><a href="uid38.html&#10;&#9;&#9;  ">A sliced inverse regression approach for data stream</a></li><li><a href="uid39.html&#10;&#9;&#9;  ">Comparison of Kernel Density Estimators with Assumption on Number of Modes</a></li><li><a href="uid40.html&#10;&#9;&#9;  ">Comparison of Kernel Density Estimators with Assumption on Number of Modes</a></li><li><a href="uid41.html&#10;&#9;&#9;  ">Comparison of sliced inverse regression approaches for underdetermined cases</a></li><li><a href="uid42.html&#10;&#9;&#9;  ">Conditional Quantile Estimationthrough Optimal Quantization</a></li><li><a href="uid43.html&#10;&#9;&#9;  ">Estimation of water consumption based on survey techniques using an automatic meter reading sample</a></li><li><a href="uid44.html&#10;&#9;&#9;  ">Hidden Markov Model for the detection of a
degraded operating mode of optronic equipment</a></li><li><a href="uid45.html&#10;&#9;&#9;  ">A new sliced inverse regression method for multivariate response</a></li><li><a href="uid46.html&#10;&#9;&#9;  ">An introduction to dimension reduction in nonparametric kernel regression</a></li></ul></div>
      <div class="TdmEntry">Bilateral Contracts and Grants with Industry<ul><li><a href="uid48.html&#10;&#9;&#9;  ">Astrium</a></li><li><a href="uid49.html&#10;&#9;&#9;  ">DCNS</a></li><li><a href="uid50.html&#10;&#9;&#9;  ">Thales Optronique</a></li></ul></div>
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	    Raweb 
	    2013</a> | <a href="http://www.inria.fr/en/teams/cqfd">Presentation of the Project-Team CQFD</a></small>
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        <h2>Section: 
      Research Program</h2>
        <h3 class="titre3">Introduction</h3>
        <p>The scientific objectives of the team are to
provide mathematical tools for modeling and optimization of complex systems.
These systems require mathematical representations which are in
essence dynamic, multi-model and stochastic. This increasing
complexity poses genuine scientific challenges in the domain of
modeling and optimization. More precisely, our research activities
are focused on stochastic optimization and (parametric,
semi-parametric, multidimensional) statistics which are complementary and interlinked topics. It is essential to develop simultaneously statistical
methods for the estimation and control methods for the optimization of the models.
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