GENERATION OF MULTIVARIATE AUTOREGRESSIVE SEQUENCES WITH EMPHASIS ON INITIAL VALUES

Date

1992-12-01

Author

ULA, TA

Metadata

Show full item record

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

Item Usage Stats

176
views

0
downloads

Certain aspects of data generation are studied through multivariate autoregressive (AR) models. The main emphasis is on the preservation of certain desired moments and the effect of initial values on these moments. The problem of preservation of moments is approached in a nontraditional way by starting with the initial values. For this purpose, general AR processes with a random start and with time-varying parameters are introduced to lay a foundation for the analysis of all types of AR processes, including the periodic cases. It is shown that an AR process with a random start and with parameters obtained from the moment equations is capable of generating jointly multivariate normal vectors with any specified means and covariance matrices, and with any specified autocovariance matrices up to a given lag. With a random start, there is no transition period involved for achieving these moments. A simple solution is proposed for matrix equations of the form BB(T) = A which appear in the moment equations. The aggregation properties of general AR process are also studied.

Subject Keywords

Water Science and Technology

URI

https://hdl.handle.net/11511/63670

Journal

JOURNAL OF HYDROLOGY

DOI

https://doi.org/10.1016/0022-1694(92)90241-m

Collections

Department of Statistics, Article

Suggestions

OpenMETU
Core

Impact of Rescaling Approaches in Simple Fusion of Soil Moisture Products Afshar, Mahdı Hesamı ; Yılmaz, Mustafa Tuğrul (American Geophysical Union (AGU), 2019-09-10) In this study, the impact of various rescaling approaches in the framework of data fusion is explored. Four different soil moisture products (Advanced Scatterometer; Advanced Microwave Scanning Radiometer for EOS, AMSR-E; Antecedent Precipitation Index; and Global Land Data Assimilation System-NOAH) are fused. The systematic differences between products are removed before the fusion utilizing various rescaling approaches focusing on different methods (regression, variance/cumulative distribution function (C...
An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models TAORMİNA, RİCCARDO; GALELLİ, STEFANO; Karakaya, Gülşah; Ahipasaoglu, S. D. (Elsevier BV, 2016-11-01) This work investigates the uncertainty associated to the presence of multiple subsets of predictors yielding data-driven models with the same, or similar, predictive accuracy. To handle this uncertainty effectively, we introduce a novel input variable selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), specifically conceived to identify all alternate subsets of predictors in a given dataset. The search process is based on a four-objective optimization problem that m...
Tracer model identification using artificial neural networks Akın, Serhat (American Geophysical Union (AGU), 2005-10-26) The derivation of transport parameters from tracer tests conducted in geothermal systems will depend strongly on the conceptual and mathematical model that is fitted to the data. Depending on the model employed the estimation of transport parameters (porosity and dispersivity of the fracture network, porosity of the matrix) may result in a significant variation in dispersivity. If the results from such tracer tests are to be used in parameter selection for larger-scale models, it is crucial that the tracer ...
Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River basin Yılmaz, Mustafa Tuğrul; Zaitchik, Ben; Hain, Chris R.; Crow, Wade T.; Ozdogan, Mutlu; Chun, Jong Ahn; Evans, Jason (American Geophysical Union (AGU), 2014-01-01) Regional evapotranspiration (ET) can be estimated using diagnostic remote sensing models, generally based on principles of energy balance closure, or with spatially distributed prognostic models that simultaneously balance both energy and water budgets over landscapes using predictive equations for land surface temperature and moisture states. Each modeling approach has complementary advantages and disadvantages, and in combination they can be used to obtain more accurate ET estimates over a variety of land...
Performance evaluation of satellite- and model-based precipitation products over varying climate and complex topography Amjad, Muhammad; Yılmaz, Mustafa Tuğrul; Yücel, İsmail; Yılmaz, Koray Kamil (Elsevier BV, 2020-05-01) Accuracy assessment of precipitation retrievals is a pre-requisite for many hydrological studies as it helps to understand the source and the magnitude of the uncertainty in hydrological response variables, particularly over regions with complex topography. This study evaluates GPM IMERGv05, TMPA 3B42V7, ERA-Interim, and ERA5 precipitation products using 256 ground-based gauge stations between 2014 and 2018 over Turkey known to have complex topography and varying climate. Error statistics, categorical perfo...

Citation Formats

T. ULA, “GENERATION OF MULTIVARIATE AUTOREGRESSIVE SEQUENCES WITH EMPHASIS ON INITIAL VALUES,” JOURNAL OF HYDROLOGY, pp. 209–233, 1992, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/63670.