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Showing 1 to 20 of 31 for “"exponential family"”.

  1. Estimation in Truncated Exponential Family of Distributions

    … estimator does not exist in a truncated negative exponential distribution on 0,T , T > 0 known, whenever the sample mean x (GREATERTHEQ) T/2.</p> <p>(ii) Broeder 1955 shows that the maximum likelihood estimator of the scale parameter of a truncated gamma distribution, with the shape parameter …

    odu Repository record for Estimation in Truncated Exponential Family of Distributions (opens in a new tab)

  2. On the choice of a model to fit data from an exponential family

    Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1984

    mit Repository record for On the choice of a model to fit data from an exponential family (opens in a new tab)

  3. Improving Inadmissible Hypothesis Testing Procedures in Exponential Family Statistical Models (Lrt, Pointwise Compactness, One-Sided Alternatives)

    This thesis is devoted to constructing tests of hypotheses that dominate a given test which violates certain conditions such as convexity or monotonicity. Many authors, for example, Birnbaum (1955), Matthes and Truax (1967), Ferguson (1967), Eaton (1970) and Marden (1981), (1982), have worked on …

    uiuc Repository record for Improving Inadmissible Hypothesis Testing Procedures in Exponential Family Statistical Models (Lrt, Pointwise Compactness, One-Sided Alternatives) (opens in a new tab)

  4. On Termination With Probability One and Bounds On Sample Size Distribution of Sequential Probability Ratio Tests Where the Underlying Model Is an Exponential Family

    Made available in DSpace on 2014-12-10T16:39:51Z (GMT). No. of bitstreams: 1 7013228.pdf: 2470993 bytes, checksum: 9dac753cd81c5e1e1c67de2dd1d86aa7 (MD5) Previous issue date: 1969

    uiuc Repository record for On Termination With Probability One and Bounds On Sample Size Distribution of Sequential Probability Ratio Tests Where the Underlying Model Is an Exponential Family (opens in a new tab)

  5. Data-Rich Personalized Causal Inference

    … introduce a framework for causal inference using exponential family modeling. In particular, we reduce answering causal questions to learning exponential family from one sample. En route, we introduce a computationally tractable alternative to maximum likelihood estimation for learning exponential

    mit Repository record for Data-Rich Personalized Causal Inference (opens in a new tab)

  6. Complete classes of two-stage estimation procedures for certain finite sample problems

    … sample procedures, for estimating the mean of exponential family distributions are given by Cohen and Sackrowitz (1984). In their study, they develop double sample Bayes estimation procedures for the mean of exponential family distributions with respect to conjugate prior distributions. The …

    uiuc Repository record for Complete classes of two-stage estimation procedures for certain finite sample problems (opens in a new tab)

  7. High-dimensional and dependent data with additional structure

    … high-dimensional multivariate time series and exponential-family random graph models. In the case of high-dimensional multivariate time series, there is often additional structure in the form of spatial structure, e.g., air pollution is monitored by monitors and the geographical locations of …

    rice Repository record for High-dimensional and dependent data with additional structure (opens in a new tab)

  8. The use of nonnull models for ranks in nonparametric statistics

    … of Mathematical Statistics) uses a one-parameter exponential family model with the canonical sufficient statistic being equivalent to Kendall's tau."

    uiuc Repository record for The use of nonnull models for ranks in nonparametric statistics (opens in a new tab)

  9. Clustering Profiles in Generalized Linear Mixed Models Settings Using Bayesian Nonparametric Statistics

    … of the response variable is a member of the exponential family. This thesis introduces a novel method for simultaneous clustering of such data and estimation of parameters of the underlying generalized linear mixed models. Generalized linear mixed models consist of two sets of parameters: …

    carleton Repository record for Clustering Profiles in Generalized Linear Mixed Models Settings Using Bayesian Nonparametric Statistics (opens in a new tab)

  10. Rates Of Escape Under Iteration Of Analytic Functions

    … the quite fast escaping set by introducing a family of sets that escape to infinity at a uniform rate associated with the maximum modulus of the function. We examine under which conditions these sets are equal to the fast escaping set, which plays an important role in the iteration of …

    the-open-u Repository record for Rates Of Escape Under Iteration Of Analytic Functions (opens in a new tab)

  11. Model selection: Consistency and robustness properties of the Schwarz Information Criterion for generalized M-estimation

    … framework to densities not belonging to the exponential family. A definition of qualitative robustness appropriate for model selection is provided and it is shown that the crucial restriction needed to achieve robustness is the uniform boundedness of the objective function defining Bias …

    uiuc Repository record for Model selection: Consistency and robustness properties of the Schwarz Information Criterion for generalized M-estimation (opens in a new tab)

  12. Bayesian Time Series Structure Learning: Formulation of an Event Driven Prior Distribution

    … of the prior distribution is within the curved exponential family. Finally, we test our developments empirically. Because traffic dynamics are comparatively accessible to common knowledge, we choose traffic time series as a test case to examine general behaviors of TIM inference and in …

    mit Repository record for Bayesian Time Series Structure Learning: Formulation of an Event Driven Prior Distribution (opens in a new tab)

  13. Scaling Bayesian inference : theoretical foundations and practical methods

    … which I call PASS-GLM, is to construct an exponential family model that approximates the original model. The data is compressed by calculating the finite-dimensional sufficient statistics of the data under the exponential family. An advantage of the compression approach to approximate …

    mit Repository record for Scaling Bayesian inference : theoretical foundations and practical methods (opens in a new tab)

  14. Efficient sequential designs with asymptotic second-order lower bound of Bayes risk for estimating product of means

    … two independent components in the one-parameter exponential family which includes the most common distribution in application of reliability testing, Bernoulli distribution. Chapter 3 also uses Monte Carlo simulations with several proposed sequential designs to illustrate optimality of the …

    umkc Repository record for Efficient sequential designs with asymptotic second-order lower bound of Bayes risk for estimating product of means (opens in a new tab)

  15. Probabilistic search: a Bayesian approach in a continuous workspace

    … a likelihood and prior belief belonging to the exponential family class, while using this class's self-conjugacy property, an exact, finite representation of the object posterior is explicitly derived. Though complexity issues may render this exact representation infeasible for computation, …

    uiuc Repository record for Probabilistic search: a Bayesian approach in a continuous workspace (opens in a new tab)

  16. First and Second Order Efficiency of Sequential Designs in a Nonlinear Situation with Applications

    … generalized under the umbrella of one-parameter exponential family. A Bayesian formulation is adopted with assumptions that the parameter are independent a priori and have conjugate prior distributions. The difficulties involved in computing explicit Bayes solutions lead to the derivation of …

    umkc Repository record for First and Second Order Efficiency of Sequential Designs in a Nonlinear Situation with Applications (opens in a new tab)

  17. Discriminative, generative, and imitative learning

    … and are provably and uniquely solvable for the exponential family. Extensions include regression, feature selection, and transduction. SVMs are also naturally subsumed and can be augmented with, for example, feature selection, to obtain substantial improvements. To extend to mixtures of …

    mit Repository record for Discriminative, generative, and imitative learning (opens in a new tab)

  18. Diskriminative Modellkombination in Spracherkennungssystemen mit großem Wortschatz

    … parameters of distributions belonging to the exponential family. It is independent of the combined models and allows for the automatic combination of any set of models of any kind. The smoothed empirical word error rate is exploitet as optimization criterion. Using the DMC method the LVCSR …

    aachen Repository record for Diskriminative Modellkombination in Spracherkennungssystemen mit großem Wortschatz (opens in a new tab)

  19. Modeling and estimation in Gaussian graphical models : maximum-entropy methods and walk-sum analysis

    … the maximum entropy relaxation (MER) within an exponential family, which maximizes entropy subject to constraints that marginal distributions on small subsets of variables are close to the prescribed marginals in relative entropy. We also present a primal-dual interior point method that is …

    mit Repository record for Modeling and estimation in Gaussian graphical models : maximum-entropy methods and walk-sum analysis (opens in a new tab)

  20. Beran Estimation of the Fisher-Bingham and Curved Kent Distributions

    … been developed for data over S^(p-1). A general exponential model which embodies some classical distributions as special cases has been proposed in high dimensions and is the subject of this dissertation. We describe the relationship between this general exponential model and the generalized form …

    guelph Repository record for Beran Estimation of the Fisher-Bingham and Curved Kent Distributions (opens in a new tab)

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