Semi-parametric Estimation of Count Time Series

Date

2014-06-27

Author

Ghahramani, Melody
Dag, Osman
de Leon, Alexander R.

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

119
views

0
downloads

A flexible semi-parametric model for autocorrelated count data is proposed. Unlike earlier models available in the literature, the model does not require construction of a likelihood function and only entails the specification of the first two conditional moments. An estimating function approach is adopted for the model. The efficiency of the estimates is investigated numerically against competing estimates via simulation studies.

Subject Keywords

Martingale, Semi-parametric, Estimating function, Autocorrelation, Count data

URI

https://hdl.handle.net/11511/66231

Collections

Department of Statistics, Conference / Seminar

Suggestions

OpenMETU
Core

Interacting multiple model probabilistic data association filter using random matrices for extended target tracking Özpak, Ezgi; Orguner, Umut; Department of Electrical and Electronics Engineering (2018) In this thesis, an Interacting Multiple Model – Probabilistic Data Association (IMM-PDA) filter for tracking extended targets using random matrices is proposed. Unlike the extended target trackers in the literature which use multiple alternative partitionings/clusterings of the set of measurements, the algorithm proposed here considers a single partitioning/clustering of the measurement data which makes it suitable for applications with low computational resources. When the IMM-PDA filter uses clustered mea...
Conceptual data modeling of multimedia database applications Aygun, S; Yazıcı, Adnan; Arica, N (1998-08-07) In this paper, we present a conceptual data model for multimedia database applications based on ExIFO(2) model. The ExIFO(2) data model is chosen as the conceptual model since it handles both complex objects along with their uncertain and imprecise properties. We enhanced this conceptual model in order to meet the multimedia data requirements. In addition to uncertain and imprecise information, we present a way of handling relationships among objects of multimedia database applications. Events that might be...
On the Cramér-Rao lower bound under model mismatch Fritsche, Carsten; Orguner, Umut; Özkan, Emre; Gustafsson, Fredrik (2014-04-24) Cram´er-Rao lower bounds (CRLBs) are proposed for deterministic parameter estimation under model mismatch conditions where the assumed data model used in the design of the estimators differs from the true data model. The proposed CRLBs are defined for the family of estimators that may have a specified bias (gradient) with respect to the assumed model. The resulting CRLBs are calculated for a linear Gaussian measurement model and compared to the performance of the maximum likelihood estimator for the corresp...
Estimation in bivariate nonnormal distributions with stochastic variance functions Tiku, Moti L.; İslam, Muhammed Qamarul; SAZAK, HAKAN SAVAŞ (Elsevier BV, 2008-01-01) Data sets in numerous areas of application can be modelled by symmetric bivariate nonnormal distributions. Estimation of parameters in such situations is considered when the mean and variance of one variable is a linear and a positive function of the other variable. This is typically true of bivariate t distribution. The resulting estimators are found to be remarkably efficient. Hypothesis testing procedures are developed and shown to be robust and powerful. Real life examples are given.
Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R Asar, Oezguer; İlk Dağ, Özlem (2014-07-01) Most of the available multivariate statistical models dictate on fitting different parameters for the covariate effects on each multiple responses. This might be unnecessary and inefficient for some cases. In this article, we propose a modelling framework for multivariate marginal models to analyze multivariate longitudinal data which provides flexible model building strategies. We show that the model handles several response families such as binomial, count and continuous. We illustrate the model on the Ke...

Citation Formats

M. Ghahramani, O. Dag, and A. R. de Leon, “Semi-parametric Estimation of Count Time Series,” 2014, p. 81, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66231.