Home  About us  Contact
 

Likelihood Estimation Procedure (likelihood + estimation_procedure)

Distribution by Scientific Domains
Mathematics and Statistics50%

Kinds of Likelihood Estimation Procedure

  • maximum likelihood estimation procedure
    		•

    Selected Abstracts

    Maximum likelihood estimation in space time bilinear models

    JOURNAL OF TIME SERIES ANALYSIS, Issue 1 2003
    YUQING DAI
    The space time bilinear (STBL) model is a special form of a multiple bilinear time series that can be used to model time series which exhibit bilinear behaviour on a spatial neighbourhood structure. The STBL model and its identification have been proposed and discussed by Dai and Billard (1998). The present work considers the problem of parameter estimation for the STBL model. A conditional maximum likelihood estimation procedure is provided through the use of a Newton,Raphson numerical optimization algorithm. The gradient vector and Hessian matrix are derived together with recursive equations for computation implementation. The methodology is illustrated with two simulated data sets, and one real-life data set. [source]

    Estimating the effect of inbreeding on survival

    ANIMAL CONSERVATION, Issue 4 2007
    D. P. Armstrong
    Abstract Conservation biologists need to be able to estimate reliably the effects of inbreeding on survival, and need to be able to do so with a range of different data types. Kalinowski and Hedrick described a non-linear maximum likelihood estimation procedure for modelling relationships between survivorship and inbreeding. Although their method is useful for illustrating the concepts involved in modelling such relationships, it is only applicable to simple datasets. We illustrate that the parameter estimates generated by Kalinowski and Hedrick's method are easily obtained using generalized linear modelling procedures available in standard statistical packages, and that these offer several advantages even with simple datasets. We suggest procedures that can be used for modelling relationships between survival and inbreeding with more complex data types, including datasets with multiple and ragged encounters, uncertain detection and random effects. [source]

    Inference for Constrained Estimation of Tumor Size Distributions

    BIOMETRICS, Issue 4 2008
    Debashis Ghosh
    Summary In order to develop better treatment and screening programs for cancer prevention programs, it is important to be able to understand the natural history of the disease and what factors affect its progression. We focus on a particular framework first outlined by Kimmel and Flehinger (1991, Biometrics, 47, 987,1004) and in particular one of their limiting scenarios for analysis. Using an equivalence with a binary regression model, we characterize the nonparametric maximum likelihood estimation procedure for estimation of the tumor size distribution function and give associated asymptotic results. Extensions to semiparametric models and missing data are also described. Application to data from two cancer studies is used to illustrate the finite-sample behavior of the procedure. [source]

    Cost Efficiency for Alberta and Ontario Dairy Farms: An Interregional Comparison

    CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS, Issue 2-3 2005
    Getu Hailu
    In this study, two non-homothetic translog stochastic meta-frontier cost functions,with and without local concavity imposed,are estimated using a nonlinear maximum likelihood estimation procedure to compare the cost efficiency of Alberta and Ontario dairy farms for the period 1984,96. The resulting cost efficiency estimates are not very sensitive to whether or not curvature is imposed. In contrast, the properties of the cost and input demand functions (e.g., elasticities) are sensitive to imposition of local concavity during estimation. The implication is that if an inappropriate model that does not satisfy the properties required by the economic theory is used, the estimated input demand functions may not be reliable. Average cost efficiency for the pooled sample, with local concavity imposed, is approximately 89%. This suggests some potential for improved performance in the sector. The results also suggest that Ontario dairy farms may be more cost efficient than Alberta dairy farms, but the statistical evidence is inconclusive. Dans la présente étude, nous avons estimé deux fonctions de coût métafrontières, stochastiques, non homothétiques de forme translogarithmique, avec et sans la concavité locale imposée, à l'aide de la procédure d'estimation du maximum de vraisemblance non linéaire pour comparer l'efficacité-coût des exploitations laitières de l'Alberta et de l'Ontario au cours de la période 1984,96. Les estimations des indices d'efficacité-coût ne sont pas très sensibles à l'imposition ou non de la concavité. En revanche, les propriétés des fonctions de coût et de demande d'intrants (ex. élasticités) sont sensibles à l'imposition de la concavité locale. La conséquence est que si on utilise un modèle incorrect qui ne respecte pas les propriétés requises par la théorie économique, les fonctions estimées de demande d'intrants peuvent ne pas être fiables. L'efficacité-coût moyen de l'échantillon total, avec la concavité locale imposée, est d'environ 89%. Ces résultats laissent supposer que certaines améliorations sont possibles dans le secteur. Ils laissent également supposer que les exploitations laitières de l'Ontario sont plus efficaces par rapport aux coûts que celles de l'Alberta, mais ces résultats ne sont pas statistiquement concluants. [source]