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1.
Book review
Title:
Book review
Author:
H. Pruscha
H. Pruscha
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Document Type:
article
URL:
http://hdl.handle.net/10.1007/BF02613595
http://hdl.handle.net/10.1007/BF02613595
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RePEc: Research Papers in Economics
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2.
Asymptotic Behaviour of Estimation Equations With Functional Nuisance Or Working Parameter
Open Access
Title:
Asymptotic Behaviour of Estimation Equations With Functional Nuisance Or Working Parameter
Author:
H. Pruscha
;
U. Wellisch
H. Pruscha
;
U. Wellisch
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Description:
INTRODUCTION The starting point of our investigations is an estimation equation of the form U n (`; ff) = 0. It contains a finite dimensional parameter ` being of primary interest and a functional parameter ff. The latter may play the role of a nuisance parameter (in the classical sense) or that of a working parameter (coming into statistical us...
INTRODUCTION The starting point of our investigations is an estimation equation of the form U n (`; ff) = 0. It contains a finite dimensional parameter ` being of primary interest and a functional parameter ff. The latter may play the role of a nuisance parameter (in the classical sense) or that of a working parameter (coming into statistical use with Liang and Zeger, 1986). A nonparametric estimator ff n Theresienstr.39, D80333 Munich, Germany 1 is assumed to be given showing a certain kind of limit behaviour, the special type of the estimator being of no regard. For estimators ` n of ` which solve (asymptotically) the estimation equation we will prove consistency and asymptotic normality. A special feature of the present paper is a consequent functionally orientated approach. The Taylor methodwell established for
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Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090414
Source:
ftp://ftp.stat.unimuenchen.de/pub/sfb386/paper79.ps.Z
ftp://ftp.stat.unimuenchen.de/pub/sfb386/paper79.ps.Z
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Document Type:
text
Language:
en
Subjects:
Some key words ; Asymptotic normality ; Consistent estimation equation estimator ; Hadamard differentiation ; Nuisance parameter ; Semiparametric estimation equation ; Semiparametric linear regression ; Working parameter
Some key words ; Asymptotic normality ; Consistent estimation equation estimator ; Hadamard differentiation ; Nuisance parameter ; Semiparametric estimation equation ; Semiparametric linear regression ; Working parameter
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DDC:
310 Collections of general statistics
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Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.2664
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.2664
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3.
Semiparametric Inference for Regression Models Based on Marked Point Processes
Open Access
Title:
Semiparametric Inference for Regression Models Based on Marked Point Processes
Author:
A. Luhm
;
H. Pruscha
A. Luhm
;
H. Pruscha
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Introduction and Basic Definitions The monography by Andersen et al. (1993) presents a kind of canonical approach to the statistical analysis of point process models. It deals with multivariate point processes where each random event carries information on the occurrence time and the type of event, the latter being from a finite set E of alterna...
Introduction and Basic Definitions The monography by Andersen et al. (1993) presents a kind of canonical approach to the statistical analysis of point process models. It deals with multivariate point processes where each random event carries information on the occurrence time and the type of event, the latter being from a finite set E of alternatives. The theoretical fundament to multivariate point processes was laid  among others  by Jacod (1975), Bremaud (1981) and Dellacherie & Meyer (1982). There are applications, however, where an uncountable set E (e.g., E the set of real numbers) of alternatives  now called marks  is more appropriate, see Scheike (1994a,b), Murphy (1995) and
Pruscha
(1997). A mathematical foundation of marked point processes (MPP's) is given by Last and Brandt (1995), but this work does not contain all tools necessary for statistical analysis. The first goal of the present paper is to fill this gap. We
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Contributors:
The Pennsylvania State University CiteSeerX Archives
Year of Publication:
20090413
Source:
ftp://ftp.stat.unimuenchen.de/pub/sfb386/paper78.ps.Z
ftp://ftp.stat.unimuenchen.de/pub/sfb386/paper78.ps.Z
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Document Type:
text
Language:
en
Subjects:
Marked point process ; intensity kernel ; locally square integrable) martingale ; local characteristic ; partial likelihood ; Mestimator
Marked point process ; intensity kernel ; locally square integrable) martingale ; local characteristic ; partial likelihood ; Mestimator
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DDC:
330 Economics
(computed)
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Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.8582
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.8582
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4.
Semiparametric Inference for Regression Models Based on Marked Point Processes
Open Access
Title:
Semiparametric Inference for Regression Models Based on Marked Point Processes
Author:
A. Luhm
;
H. Pruscha
A. Luhm
;
H. Pruscha
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Description:
SUMMARY. We study marked point processes (MPP's) with an arbitrary mark space. First we develop some statistically relevant topics in the theory of MPP's admitting an intensity kernel t(dz), namely martingale results, central limit theorems for both the number n of objects under observation and the time t tending to in nity, the decomposition in...
SUMMARY. We study marked point processes (MPP's) with an arbitrary mark space. First we develop some statistically relevant topics in the theory of MPP's admitting an intensity kernel t(dz), namely martingale results, central limit theorems for both the number n of objects under observation and the time t tending to in nity, the decomposition into a local characteristic ( t � t(dz)) and a likelihood approach. Then we present semiparametric statistical inference in a class of Aalen (1975)type multiplicative regression models for MPP's as n!1, using partial likelihood methods. Furthermore, considering the case t!1,we study purely parametric Mestimators.
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Year of Publication:
20080814
Source:
http://epub.ub.unimuenchen.de/1472/1/paper_78.pdf
http://epub.ub.unimuenchen.de/1472/1/paper_78.pdf
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Document Type:
text
Language:
en
Subjects:
Marked point process ; intensity kernel ; locally square integrable) martingale ; local characteristic ; partial likelihood ; Mestimator
Marked point process ; intensity kernel ; locally square integrable) martingale ; local characteristic ; partial likelihood ; Mestimator
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Rights:
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.111.4329
http://epub.ub.unimuenchen.de/1472/1/paper_78.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.111.4329
http://epub.ub.unimuenchen.de/1472/1/paper_78.pdf
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5.
Book reviews
Title:
Book reviews
Author:
N. Obata
;
E. Collani
;
S. Csörgö
;
H. Pruscha
;
D. Burkholder
;
W. Weil
;
R. Schaßberger
;
C. Deniau
;
P. Meyer
N. Obata
;
E. Collani
;
S. Csörgö
;
H. Pruscha
;
D. Burkholder
;
W. Weil
;
R. Schaßberger
;
C. Deniau
;
P. Meyer
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Document Type:
article
URL:
http://hdl.handle.net/10.1007/BF01895330
http://hdl.handle.net/10.1007/BF01895330
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6.
Probability and Statistics. Theory and Applications  G. Blom.
Title:
Probability and Statistics. Theory and Applications  G. Blom.
Author:
Pruscha, H.
Pruscha, H.
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Source:
Pruscha
, H.;: Metrika. 38 1991
Pruscha
, H.;: Metrika. 38 1991
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Document Type:
Text
Subjects:
510.Mathematics
510.Mathematics
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Rights:
Open Access ; Mathematics
Open Access ; Mathematics
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Relations:
Book Reviews.
URL:
http://resolver.sub.unigoettingen.de/purl?PPN358794056_0038/dmdlog9
http://resolver.sub.unigoettingen.de/purl?PPN358794056_0038/dmdlog9
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7.
Topics in Statistical Methodology  S. Biswas.
Title:
Topics in Statistical Methodology  S. Biswas.
Author:
Pruscha, H.
Pruscha, H.
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Source:
Pruscha
, H.;: Metrika. 41 1994
Pruscha
, H.;: Metrika. 41 1994
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Document Type:
Text
Subjects:
510.Mathematics
510.Mathematics
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Rights:
Open Access ; Mathematics
Open Access ; Mathematics
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Relations:
Book Reviews.
URL:
http://resolver.sub.unigoettingen.de/purl?GDZPPN002466694
http://resolver.sub.unigoettingen.de/purl?PPN358794056_0041/dmdlog74
http://resolver.sub.unigoettingen.de/purl?GDZPPN002466694
http://resolver.sub.unigoettingen.de/purl?PPN358794056_0041/dmdlog74
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8.
Semiparametric Estimation in Regression Models for Point Processes based on One Realization
Title:
Semiparametric Estimation in Regression Models for Point Processes based on One Realization
Author:
Pruscha, H.
Pruscha, H.
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Description:
We are dealing with regression models for point processes having a multiplicative intensity process of the form alpha(t) * b_t . The deterministic function alpha describes the longterm trend of the process. The stochastic process b accounts for the shortterm random variations and depends on a finitedimensional parameter. The semiparametric es...
We are dealing with regression models for point processes having a multiplicative intensity process of the form alpha(t) * b_t . The deterministic function alpha describes the longterm trend of the process. The stochastic process b accounts for the shortterm random variations and depends on a finitedimensional parameter. The semiparametric estimation procedure is based on one single observation over a long time interval. We will use penalized estimation functions to estimate the trend alpha, while the likelihood approach to point processes is employed for the parametric part of the problem. Our methods are applied to earthquake data as well as to records on 24hours ECG.
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Year of Publication:
19970101
Document Type:
doctype:workingPaper ; Paper ; NonPeerReviewed
Subjects:
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
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Relations:
http://epub.ub.unimuenchen.de/1460/1/paper_66.pdf ; Pruscha, H. (1997): Semiparametric Estimation in Regression Models for Point Processes based on One Realization. Sonderforschungsbereich 386, Discussion Paper 66
URL:
http://epub.ub.unimuenchen.de/1460/
http://nbnresolving.de/urn/resolver.pl?urn=nbn:de:bvb:19epub14600
http://epub.ub.unimuenchen.de/1460/
http://nbnresolving.de/urn/resolver.pl?urn=nbn:de:bvb:19epub14600
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9.
Semiparametric Point Process and Time Series Models for Series of Events
Title:
Semiparametric Point Process and Time Series Models for Series of Events
Author:
Pruscha, H.
Pruscha, H.
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Description:
We are dealing with series of events occurring at random times tau_n and carrying further quantitive information xi_n . Examples are sequences of extrasystoles in ECGrecords. We will present two approaches for analyzing such (typically long) sequences (tau_n, xi_n ), n = 1, 2, . . (i) A point process model is based on an intensity of the form a...
We are dealing with series of events occurring at random times tau_n and carrying further quantitive information xi_n . Examples are sequences of extrasystoles in ECGrecords. We will present two approaches for analyzing such (typically long) sequences (tau_n, xi_n ), n = 1, 2, . . (i) A point process model is based on an intensity of the form alpha(t) * b_t(theta), t >= 0, with b_t a stochastic intensity of the selfexciting type. (ii) A time series approach is based on a transitional GLM. The conditional expectation of the waiting time sigma_{n+1} = tau_{n+1}  tau_n is set to be v(tau_n) * h(eta_n(theta)), with h a response function and eta_n a regression term. The deterministic functions alpha and v, respectively, describe the longterm trend of the process.
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Year of Publication:
19980101
Document Type:
doctype:workingPaper ; Paper ; NonPeerReviewed
Subjects:
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
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Relations:
http://epub.ub.unimuenchen.de/1503/1/paper_114.pdf ; Pruscha, H. (1998): Semiparametric Point Process and Time Series Models for Series of Events. Sonderforschungsbereich 386, Discussion Paper 114
URL:
http://epub.ub.unimuenchen.de/1503/
http://nbnresolving.de/urn/resolver.pl?urn=nbn:de:bvb:19epub15030
http://epub.ub.unimuenchen.de/1503/
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10.
Residual and forecast methods in time series models with covariates
Title:
Residual and forecast methods in time series models with covariates
Author:
Pruscha, H.
Pruscha, H.
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Description:
We are dealing with time series which are measured on an arbitrary scale, e.g. on a categorical or ordinal scale, and which are recorded together with time varying covariates. The conditional expectations are modelled as a regression model, its parameters are estimated via likelihood or quasilikelihoodapproach. Our main concern are diagnostic...
We are dealing with time series which are measured on an arbitrary scale, e.g. on a categorical or ordinal scale, and which are recorded together with time varying covariates. The conditional expectations are modelled as a regression model, its parameters are estimated via likelihood or quasilikelihoodapproach. Our main concern are diagnostic methods and forecasting procedures for such time series models. Diagnostics are based on (partial) residual measures as well as on (partial) residual variables; lstep predictors are gained by an approximation formula for conditional expectations. The various methods proposed are illustrated by two different data sets.
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Year of Publication:
19960101
Document Type:
doctype:workingPaper ; Paper ; NonPeerReviewed
Subjects:
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
Sonderforschungsbereich 386 ; Sonderforschungsbereich 386 ; ddc:510
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Relations:
http://epub.ub.unimuenchen.de/1434/1/paper_33.pdf ; Pruscha, H. (1996): Residual and forecast methods in time series models with covariates. Sonderforschungsbereich 386, Discussion Paper 33
URL:
http://epub.ub.unimuenchen.de/1434/
http://nbnresolving.de/urn/resolver.pl?urn=nbn:de:bvb:19epub14346
http://epub.ub.unimuenchen.de/1434/
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(10) Pruscha, H.
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(2) A. Luhm
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(7) sonderforschungsbereich 386
(2) 510 mathematics
(2) intensity kernel
(2) local characteristic
(2) locally square integrable martingale
(2) m estimator
(2) marked point process
(2) partial likelihood
(1) asymptotic normality
(1) consistent estimation equation estimator
(1) hadamard differentiation
(1) nuisance parameter
(1) semiparametric estimation equation
(1) semiparametric linear regression
(1) some key words
(1) working parameter
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(1) Economics [33*]
(1) Mathematics [51*]
(1) Medicine & health [61*]
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