16 results on '"Philippe, Anne"'
Search Results
2. Random discretization of stationary continuous time processes
- Author
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Philippe, Anne, Robet, Caroline, and Viano, Marie-Claude
- Published
- 2021
- Full Text
- View/download PDF
3. A frequency-domain test for long range dependence
- Author
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Gromykov, Gennadi, Ould Haye, Mohamedou, and Philippe, Anne
- Published
- 2018
- Full Text
- View/download PDF
4. Non-Informative Priors in the Case of Gaussian Long-Memory Processes
- Author
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Philippe, Anne and Rousseau, Judith
- Published
- 2002
5. Some convergence results on quadratic forms for random fields and application to empirical covariances
- Author
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Lavancier, Frédéric and Philippe, Anne
- Published
- 2011
- Full Text
- View/download PDF
6. Inference for continuous-time long memory randomly sampled processes
- Author
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Ould Haye, Mohamedou, Philippe, Anne, Robet, Caroline, School of Mathematics and Statistics [Ottawa], Carleton University, Laboratoire de Mathématiques Jean Leray (LMJL), Centre National de la Recherche Scientifique (CNRS)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), and Université de Nantes (UN)-Université de Nantes (UN)
- Subjects
Long memory ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,stationarity ,FOS: Mathematics ,limit theorems ,Continuous-time Gaussian processes ,Mathematics - Statistics Theory ,Statistics Theory (math.ST) ,dependence ,time series ,sampled process - Abstract
From a continuous-time long memory stochastic process, a discrete-time randomly sampled one is drawn. We investigate the second-order properties of this process and establish some time-and frequency-domain asymptotic results. We mainly focus on the case when the initial process is Gaussian. The challenge being that, although marginally remains Gaussian, the randomly sampled process will no longer be jointly Gaussian.
- Published
- 2019
7. Frequency approach for detecting nonstationarity in dependent data
- Author
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Ould Haye, mohamedou, Philippe, Anne, School of Mathematics and Statistics [Carleton University], Carleton University, Laboratoire de Mathématiques Jean Leray (LMJL), Centre National de la Recherche Scientifique (CNRS)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), and Université de Nantes (UN)-Université de Nantes (UN)
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random walk ,unit root ,frenquency ,Long memory ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,stationarity ,limit theorem ,dependence ,time series ,hypothesis test - Abstract
Distinguishing long memory behaviour from nonstationarity can be very difficult as in both cases the sample autocovariance function decays very slowly. Available stationarity tests either do not include long memory or fare poorly in terms of empirical size, especially near the boundary between long memory and nonstationarity. We propose a parameter- free decision rule, that is based on evaluating periodograms at different epochs. We establish some asymptotic theorems in order to validate the method. Limiting distribu- tions are easily tractable as sum of weighted independent χ2 random variables. Moreover, numerical studies are provided to show that the proposed approach outperforms existing methods. We also apply our method to a well-known empirical data, often cited as an example of confusion between long memory and nonstationarity.
- Published
- 2019
8. Aggregation and long memory: recent developments
- Author
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Leipus, Remigijus, Philippe, Anne, Puplinskaite, Donata, Surgailis, Donatas, Vilnius Institute of Mathematics and Informatics, Vilnius University [Vilnius], Laboratoire de Mathématiques Jean Leray (LMJL), Centre National de la Recherche Scientifique (CNRS)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN), and Grant (No. MIP-13079) from the Research Council of Lithuania.
- Subjects
autoregressive random fields ,mixed moving average ,anisotropic long memory ,Random-coefficient AR(1) ,long memory ,Mathematics - Statistics Theory ,Statistics Theory (math.ST) ,contemporaneous aggregation ,[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] ,intermediate process ,disaggregation ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,FOS: Mathematics ,infinite variance ,scaling limit - Abstract
It is well-known that the aggregated time series might have very different properties from those of the individual series, in particular, long memory. At the present time, aggregation has become one of the main tools for modelling of long memory processes. We review recent work on contemporaneous aggregation of random-coefficient AR(1) and related models, with particular focus on various long memory properties of the aggregated process.
- Published
- 2013
9. Contemporaneous aggregation of triangular array of random-coefficient AR(1) processes
- Author
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Philippe, Anne, Puplinskaite, Donata, Surgailis, Donatas, Laboratoire de Mathématiques Jean Leray (LMJL), Centre National de la Recherche Scientifique (CNRS)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN), Vilnius Institute of Mathematics and Informatics, and Vilnius University [Vilnius]
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Aggregation ,random-coefficient AR(1) process ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,partial sums process ,FOS: Mathematics ,infinitely divisible distribution ,long memory ,Mathematics - Statistics Theory ,Statistics Theory (math.ST) ,[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] - Abstract
We discuss contemporaneous aggregation of independent copies of a triangular array of random-coefficient AR(1) processes with i.i.d. innovations belonging to the domain of attraction of an infinitely divisible law W. The limiting aggregated process is shown to exist, under general assumptions on W and the mixing distribution, and is represented as a mixed infinitely divisible moving-average. Partial sums process of $ is discussed under the assumption E( W^2 ) is finite and a mixing density regularly varying at the ''unit root'' x=1 with exponent \beta >0. We show that the above partial sums process may exhibit four different limit behaviors depending on \beta and the Lévy triplet of W. Finally, we study the disaggregation problem in spirit of Leipus et al. (2006) and obtain the weak consistency of the corresponding estimator of the mixing distribution in a suitable L_2-space.
- Published
- 2013
10. Detection of non-constant long memory parameter
- Author
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Lavancier, Fr��d��ric, Leipus, Remigijus, Philippe, Anne, Surgailis, Donatas, Laboratoire de Mathématiques Jean Leray (LMJL), Centre National de la Recherche Scientifique (CNRS)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN), Vilnius Institute of Mathematics and Informatics, and Vilnius University [Vilnius]
- Subjects
change in persistence ,Long memory ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,FOS: Mathematics ,ratio test ,V/S statistic ,change point ,Mathematics - Statistics Theory ,Statistics Theory (math.ST) ,fractional integration ,[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] - Abstract
This article deals with detection of nonconstant long memory parameter in time series. The null hypothesis presumes stationary or nonstationary time series with constant long memory parameter, typically an I(d) series with d>-.5. The alternative corresponds to an increase in persistence and includes in particular an abrupt or gradual change from I(d_1) to I(d_2).
- Published
- 2012
11. CONTEMPORANEOUS AGGREGATION OF TRIANGULAR ARRAY OF RANDOM-COEFFICIENT AR(1) PROCESSES.
- Author
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Philippe, Anne, Puplinskaite, Donata, and Surgailis, Donatas
- Subjects
- *
AGGREGATION (Statistics) , *TRIANGULARIZATION (Mathematics) , *MULTILEVEL models , *MIXTURE distributions (Probability theory) , *PARTIAL sums (Series) , *INFINITE mixture models (Statistics) - Abstract
We discuss contemporaneous aggregation of independent copies of a triangular array of random-coefficient processes with i.i.d. innovations belonging to the domain of attraction of an infinitely divisible law W. The limiting aggregated process is shown to exist, under general assumptions on W and the mixing distribution, and is represented as a mixed infinitely divisible moving average [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
12. A two-sample test for comparison of long memory parameters
- Author
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Lavancier, Frédéric, Philippe, Anne, and Surgailis, Donatas
- Subjects
- *
ECONOMETRICS , *STATISTICAL sampling , *COMPARATIVE studies , *PARAMETER estimation , *GEOMETRICAL constructions , *ANALYSIS of covariance - Abstract
Abstract: We construct a two-sample test for comparison of long memory parameters based on ratios of two rescaled variance (V/S) statistics studied in Giraitis et al. [L. Giraitis, R. Leipus, A. Philippe, A test for stationarity versus trends and unit roots for a wide class of dependent errors, Econometric Theory 21 (2006) 989–1029]. The two samples have the same length and can be mutually independent or dependent. In the latter case, the test statistic is modified to make it asymptotically free of the long-run correlation coefficient between the samples. To diminish the sensitivity of the test on the choice of the bandwidth parameter, an adaptive formula for the bandwidth parameter is derived using the asymptotic expansion in Abadir et al. [K. Abadir, W. Distaso, L. Giraitis, Two estimators of the long-run variance: beyond short memory, Journal of Econometrics 150 (2009) 56–70]. A simulation study shows that the above choice of bandwidth leads to a good size of our comparison test for most values of fractional and ARMA parameters of the simulated series. [Copyright &y& Elsevier]
- Published
- 2010
- Full Text
- View/download PDF
13. Asymptotic normality of the mixture density estimator in a disaggregation scheme.
- Author
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Celov, Dmitrij, Leipus, Remigijus, and Philippe, Anne
- Subjects
ASYMPTOTIC expansions ,DIOPHANTINE analysis ,NUMBER theory ,MATHEMATICAL statistics ,ESTIMATION theory - Abstract
The paper concerns the asymptotic distribution of the mixture density estimator, proposed by Leipus et al. [Leipus, R., Oppenheim, G., Philippe, A., and Viano, M.-C. (2006), 'Orthogonal Series Density Estimation in a Disaggregation Scheme', Journal of Statistical Planning and Inference, 136, 2547-2571], in the aggregation/disaggregation problem of random parameter AR(1) process. We prove that, under mild conditions on the (semiparametric) form of the mixture density, the estimator is asymptotically normal. The proof is based on the limit theory for the quadratic form in linear random variables developed by Bhansali et al. [Bhansali, R.J., Giraitis, L., and Kokoszka, P.S. (2007), Approximations and Limit Theory for Quadratic Forms of Linear Processes', Stochastic Processes and their Applications, 117, 71-95]. The moving average representation of the aggregated process is investigated. A simulation study illustrates the result. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
14. Orthogonal series density estimation in a disaggregation scheme
- Author
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Leipus, Remigijus, Oppenheim, George, Philippe, Anne, and Viano, Marie-Claude
- Subjects
- *
POLYNOMIALS , *MATHEMATICAL models , *MONTE Carlo method , *MATHEMATICS - Abstract
Abstract: In this paper we consider long-memory processes obtained by aggregation of independent random parameter AR(1) processes. We propose an estimator of the density of the underlying random parameter. This estimator is based on the expansion of the density function on the basis of Gegenbauer polynomials. Rate of convergence to zero of the mean integrated square error (MISE) and of the uniform error are obtained. The results are illustrated by Monte-Carlo simulations. [Copyright &y& Elsevier]
- Published
- 2006
- Full Text
- View/download PDF
15. Autoregresinių procesų ir atsitiktinių laukų su baigtine arba begaline dispersija agregavimas
- Author
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Puplinskaitė, Donata, PAULAUSKAS, VYGANTAS, DAVYDOV, YOURI, LEIPUS, REMIGIJUS, RAČKAUSKAS, ALFREDAS, SUQUET, CHARLES, JAKUBOWKI, ADAM, SOULIER, PHILIPPE, SURGAILIS, DONATAS, PHILIPPE, ANNE, and Vilnius University
- Subjects
Random process ,Aggregation ,Long memory ,long memory ,random process ,nearest-neighbour random fields ,Atsitiktiniai procesai ,Artimiausio kaimyno atsitiktiniai laukai ,Nearest-neighbour random fields ,Ilga atmintis ,Agregavimas ,Mathematics - Abstract
Aggregated data appears in many areas such as econimics, sociology, geography, etc. This motivates an importance of studying the (dis)aggregation problem. One of the most important reasons why the contemporaneous aggregation become an object of research is the possibility of obtaining the long memory phenomena in processes. The aggregation provides an explanation of the long-memory effect in time series and a simulation method of such series as well. Accumulation of short-memory non-ergodic random processes can lead to the long memory ergodic process, that can be used for the forecasts of the macro and micro variables. We explore the aggregation scheme of AR(1) processes and nearest-neighbour random fields with infinite variance. We provide results on the existence of limit aggregated processes, and find conditions under which it has long memory properties in certain sense. For the random fields on Z^2, we introduce the notion of (an)isotropic long memory based on the behavior of partial sums. In L_2 case, the known aggregation of independent AR(1) processes leads to the Gaussian limit. While we describe a new model of aggregation based on independent triangular arrays. This scheme gives the limit aggregated process with finite variance which is not necessary Gaussian. We study a discrete time risk insurance model with stationary claims, modeled by the aggregated heavy-tailed process. We establish the asymptotic properties of the ruin probability and the dependence structure... [to full text] Agreguoti duomenys naudojami daugelyje mokslo sričių tokių kaip ekonomika, sociologija, geografija ir kt. Tai motyvuoja tirti (de)agregavimo uždavinį. Viena iš pagrindinių priežasčių kodėl vienalaikis agregavimas tapo tyrimų objektu yra galimybė gauti ilgos atminties procesus. Agregavimas paaiškina ilgos atminties atsiradima procesuose ir yra vienas iš būdų tokius procesus generuoti. Agreguodami trumpos atminties neergodiškus atsitiktinius procesus, galime gauti ilgos atminties ergodišką procesą, kuris gali būti naudojamas mikro ir makro kintamųjų prognozavimui. Disertacijoje nagrinėjama AR(1) procesų bei artimiausio kaimyno atsitiktinių laukų, turinčių begalinę dispersiją, agregavimo schema, randamos sąlygos, kurioms esant ribinis agreguotas procesas egzistuoja, ir turi ilgąją atmintį tam tikra prasme. Atsitiktinių laukų atveju, įvedamas anizotropinės/izotropinės ilgos atminties apibrėžimas, kuris yra paremtas dalinių sumų elgesiu. Baigtinės dispersijos atveju yra gerai žinoma nepriklausomų AR(1) procesų schema, kuri rezultate duoda Gauso ribinį agreguotą procesą. Disertacijoje aprašoma trikampio masyvo agregavimo modelis, kuris baigtinės dispersijos atveju duoda nebūtinai Gauso ribinį agreguotą procesą. Taip pat disertacijoje nagrinėjama bankroto tikimybės asimptotika, kai žalos yra aprašomos sunkiauodegiu agreguotu procesu, nusakoma priklausomybė tarp žalų, apibūdinama žalų ilga atmintis.
- Published
- 2013
16. Aggregation of autoregressive processes and random fields with finite or infinite variance
- Author
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Puplinskaitė, Donata, SURGAILIS, DONATAS, PAULAUSKAS, VYGANTAS, DAVYDOV, YOURI, LEIPUS, REMIGIJUS, RAČKAUSKAS, ALFREDAS, SUQUET, CHARLES, JAKUBOWKI, ADAM, SOULIER, PHILIPPE, PHILIPPE, ANNE, and Vilnius University
- Subjects
Random process ,Aggregation ,Long memory ,Atsitiktinis procesas ,long memory ,random process ,nearest-neighbour random fields ,Ilga atmintis ,Nearest-neighbour random fields ,Agregavimas ,Artimiausio kaimynio laukai ,Mathematics - Abstract
Agreguoti duomenys naudojami daugelyje mokslo sričių tokių kaip ekonomika, sociologija, geografija ir kt. Tai motyvuoja tirti (de)agregavimo uždavinį. Viena iš pagrindinių priežasčių kodėl vienalaikis agregavimas tapo tyrimų objektu yra galimybė gauti ilgos atminties procesus. Agregavimas paaiškina ilgos atminties atsiradima procesuose ir yra vienas iš būdų tokius procesus generuoti. Agreguodami trumpos atminties neergodiškus atsitiktinius procesus, galime gauti ilgos atminties ergodišką procesą, kuris gali būti naudojamas mikro ir makro kintamųjų prognozavimui. Disertacijoje nagrinėjama AR(1) procesų bei artimiausio kaimyno atsitiktinių laukų, turinčių begalinę dispersiją, agregavimo schema, randamos sąlygos, kurioms esant ribinis agreguotas procesas egzistuoja, ir turi ilgąją atmintį tam tikra prasme. Atsitiktinių laukų atveju, įvedamas anizotropinės/izotropinės ilgos atminties apibrėžimas, kuris yra paremtas dalinių sumų elgesiu. Baigtinės dispersijos atveju yra gerai žinoma nepriklausomų AR(1) procesų schema, kuri rezultate duoda Gauso ribinį agreguotą procesą. Disertacijoje aprašoma trikampio masyvo agregavimo modelis, kuris baigtinės dispersijos atveju duoda nebūtinai Gauso ribinį agreguotą procesą. Taip pat disertacijoje nagrinėjama bankroto tikimybės asimptotika, kai žalos yra aprašomos sunkiauodegiu agreguotu procesu, nusakoma priklausomybė tarp žalų, apibūdinama žalų ilga atmintis. Aggregated data appears in many areas such as econimics, sociology, geography, etc. This motivates an importance of studying the (dis)aggregation problem. One of the most important reasons why the contemporaneous aggregation become an object of research is the possibility of obtaining the long memory phenomena in processes. The aggregation provides an explanation of the long-memory effect in time series and a simulation method of such series as well. Accumulation of short-memory non-ergodic random processes can lead to the long memory ergodic process, that can be used for the forecasts of the macro and micro variables. We explore the aggregation scheme of AR(1) processes and nearest-neighbour random fields with infinite variance. We provide results on the existence of limit aggregated processes, and find conditions under which it has long memory properties in certain sense. For the random fields on Z^2, we introduce the notion of (an)isotropic long memory based on the behavior of partial sums. In L_2 case, the known aggregation of independent AR(1) processes leads to the Gaussian limit. While we describe a new model of aggregation based on independent triangular arrays. This scheme gives the limit aggregated process with finite variance which is not necessary Gaussian. We study a discrete time risk insurance model with stationary claims, modeled by the aggregated heavy-tailed process. We establish the asymptotic properties of the ruin probability and the dependence structure... [to full text]
- Published
- 2013
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