Turkman, K. Feridun.
Turkman, Kamil Feridun
Turkman, K.F.
VIAF ID: 49313738 ( Personal )
Permalink: http://viaf.org/viaf/49313738
Preferred Forms
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- 200 _ | ‡a Turkman ‡b K. Feridun
- 100 1 _ ‡a Turkman, K. Feridun
- 100 1 _ ‡a Turkman, K. Feridun
- 100 1 _ ‡a Turkman, K. Feridun
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- 100 1 _ ‡a Turkman, K. Feridun
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- 100 1 0 ‡a Turkman, K. Feridun
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- 100 1 _ ‡a Turkman, K.F.
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4xx's: Alternate Name Forms (6)
Works
Title | Sources |
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Análise de sucessões cronológicas | |
An application of optimal screening methods to respiratory illnesses | |
Asymptotic models and inference for extremes of spatio-temporal models | |
Bayesian approach to event prediction | |
Bayesian extreme quantile estimation for heavy-tailed distributions | |
bayesian hierarchical model for over-dispersed count data a case study for abundance of hake recruits | |
Extremal behaviour of trigonometric polynomials with random coefficients | |
Extremes of continuous processes and their discrete skeletons | |
Extremes of Volterra series expansions with heavy-tailed innovations | |
Maximum of a stationary random field over finite rectangles | |
Métodos probabilísticos e estatísticos na gestão de fogos florestais | |
Modelos bayesianos hierárquicos no planeamento de recursos humanos | |
Non-Linear Time Series : Extreme Events and Integer Value Problems | |
note on the extremal index for space-time processes | |
On the extremal behavior of sub-sampled solutions of stochastic difference equat | |
Optimal alarm systems for AR (p) processes bayesian approach | |
Parameter and observation driven models for predicting precipitation | |
Pollution assessment and control | |
Preliminary analysis of the forest in Portugal using point processes | |
Recursos humanos da FCUL presente e futuro | |
Some problems in bayesian hierarchical modeling of non-Gaussian spatial-temporal data | |
Spatio-temporal methods and models for unemployment estimation | |
Stat. env. | |
Statistics for the environment, c1993: | |
study of the air quality in Lisbon a spatio-temporal model for the prediction of NO2 Levels |