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See detailModelling the climate effect on Black Grouse Population Dynamic in Rhön Biopshere Reserve
Loneux, Michèle ULg; Kolb, Karl-Heinz; Lindsey, James ULg

in Plummer, Ron (Ed.) Black Grouse Endangered Species (2005, December)

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See detailModelling the Influence of Climate on Black Grouse Population Dynamics in Lüneburger Heide (North Germany)
Loneux, Michèle ULg; Lütkepohl, Manfred; Wübbenhorst, Jann et al

in PLUMMER, Ron (Ed.) European Conference Black Grouse Endangered species (2005, December)

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See detailClimatic influence on black grouse population dynamic in Belgian Hautes-Fagnes.
Loneux, Michèle ULg; Lindsey, James ULg; Vandiepenbeeck, Marc et al

in Sylvia (2003), 39(supplement), 53-57

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See detailClimatic Modelling of Black Grouse population dynamics: a game or a tool?
Loneux, Michèle ULg; Lindsey, James ULg

in Sylvia (2003), 39(supplement), 43-52

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See detailAnalysing financial returns using regression models based on non-symmetric stable distributions
Lambert, Philippe ULg; Lindsey, James ULg

in Journal of the Royal Statistical Society : Series C (Applied Statistics) (1999), 48

The daily evolution of the price of Abbey National shares over a 10-week period is analysed by using regression models based on possibly non-symmetric stable distributions. These distributions, which are ... [more ▼]

The daily evolution of the price of Abbey National shares over a 10-week period is analysed by using regression models based on possibly non-symmetric stable distributions. These distributions, which are only known through their characteristic function, can be used in practice for interactive modelling of heavy-tailed processes. A regression model for the location parameter is proposed and shown to induce a similar model for the mode. Finally, regression models for the other three parameters of the stable distribution are introduced. The model found to fit best allows the skewness of the distribution, rather than the location or scale parameters, to vary over time. The most likely share return is thus changing over time although the region where most returns are observed is stationary. [less ▲]

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See detailOn the appropriateness of marginal models for repeated measurements in clinical trials
Lindsey, James ULg; Lambert, Philippe ULg

in Statistics in Medicine (1998), 17

Although models developed directly to describe marginal distributions have become widespread in the analysis of repeated measurements, some of their disadvantages are not well enough known. These include ... [more ▼]

Although models developed directly to describe marginal distributions have become widespread in the analysis of repeated measurements, some of their disadvantages are not well enough known. These include producing profile curves that correspond to no possible individual, possibly showing that a treatment is superior on average when it is poorer for each individual subject, implicitly generating complex and implausible physiological explanations, including underdispersion in subgroups, and sometimes corresponding to no possible probabilistic data generating mechanism. We conclude that such marginal models may sometimes be appropriate for descriptive observational studies, such as sample surveys in epidemiology, but should only be used with great care in causal experimental settings, such as clinical trials. [less ▲]

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See detailInfluence du climat sur l'évolution de la population de tétras lyres Tetrao tetrix dans les Hautes-Fagnes de Belgique de 1967 à 1996
Loneux, Michèle ULg; Lindsey, James ULg; Ruwet, Jean-Claude

in Cahiers d'Ethologie (1997), 17(2-3-4), 345-386

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See detailDynamic generalized linear models and repeated measurements
Lindsey, James ULg; Lambert, Philippe ULg

in Journal of Statistical Planning & Inference (1995), 47

The dynamic generalized linear model for non-normal data is extended for use in repeated measurements, when series of observations are available for more than one individual. Examples are given for count ... [more ▼]

The dynamic generalized linear model for non-normal data is extended for use in repeated measurements, when series of observations are available for more than one individual. Examples are given for count and duration data. [less ▲]

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