Search Results

1 - 10 of 539 items :

  • "statistical model" x
Clear All

. What is a statistical model? The Annals of Statistics 30: 1225–67. Moneta, Alessio; and Russo, Federica. 2014. Causal models and evidential pluralism in econometrics. Journal of Economic Methodology 21: 54–76. Musek, Janek. 2007. A general factor of personality: evidence for the big one in the five-factor model. Journal of Research in Personality 41: 1213–33. Reichenbach, Hans. 1956. The Direction of Time . Los Angeles: University of California Press. van der Maas, Han. L. J.; Dolan, Conor. V.; Grasman, Raoul. P.; Wicherts, Jelte. M.; Huizenga, Hilde. M.; and

1 Business Systems Research | Vol. 10 No. 2 |2019 Editorial for the special issue: “Advances and Trends in Post-transition Countries: Statistical Modelling Approach” Ksenija Dumičić Department of Statistics, Faculty of Economics and Business, University of Zagreb, Zagreb, Croatia Blagica Novkovska Faculty of Economics, University of Tourism and Management, Skopje, Republic of North Macedonia Emina Resić School of Economics and Business of University of Sarajevo, Bosnia and Herzegovina Abstract This special issue of Business

distribution: an interface between ecological theory and statistical modelling. – Ecol. Modelling 157: 101-118. Austin, M.P. 2005. Vegetation and environment: discontinuities and continuities. – In: van der Maarel, E. (ed.), Vegetation ecology, Blackwell, Oxford, pp. 52-84. Austin, M.P., Belbin, L., Meyers, J.A., Doherty, M.D. & Luoto, M. 2006. Evaluation of statistical models used for predicting plant species distributions: role of artificial data and theory. – Ecol. Modelling 199: 197-216. Austin, M.P., Cunningham, R.B. & Fleming, P.M. 1984. New approaches to direct

latent vector analysis to pulp characterization. PAP Puu-Pup Tim. 77(6/7), 410-418. 11. Broderick, G., Paris, J., Valade, J.L. & Wood, J. (1996). Linking the fi ber characteristics and handsheet properties of a high-yield pulp. TAPPI J. 79(1), 161-169. 12. Grage, H. (2004). A statistical analysis of data from the production line at the Munksund paper mill. Technical report, Lund Institute of Technology, Sweden. 13. Nordstrom, F., Lindstrom, T. & Holst, J. (2005). Statistical models for on-line monitoring quality properties. Technical report, Lund Institute of

into computerized models together with the organ characteristics and the electrodes configuration. These calculations traditionally use deterministic models, i.e all the cells exposed to electrical fields higher than a specific threshold, known in the literature, will be irreversibly/reversibly electroporated. Nevertheless, live tissues are more complex, especially malignant tissues which are inherently inhomogeneous, and therefore assuming a statistical effect of EP parameters maybe more appropriate. 32 , 33 For this reason we chose to apply a statistical model

). Solving the Transient Cost-Related Optimization Problem for Copper Flash Smelting Process with Legendre Pseudospectral Method. Mater. Trans. , 54(3), 350–356. DO I: 10.2320/matertrans.M2012350. 18. Živković, Ž., Mihajlović I. & Nikolić Đ. (2009). Artificial neural network method applied on the nonlinear multivariate problems. Serb. J. Manag., 4, 143–155. 19. Živković, Ž., Mihajlović, I., Djurić, I. & Štrbac, N. (2010). Statistical modeling of the industrial sodium aluminate solutions decomposition process. Metall. Mater. Trans. B, 41, 1116–1122. DOI: 10.1007/s11663

References [1] FIˇ SEROV´A, E.-KUB´A ˇC EK, L.-KUNDEROV´A, P.: Linear Statistical Models: Regularity and Singularities. Academia, Praha, 2007. [2] HUMAK, K. M. S.: Statistische Methoden der Modellbildung, Band 1. Akademie-Verlag, Berlin, 1977. [3] KSHIRSAGAR, A. M.: Multivariate Analysis. Marcel Dekker, Inc., New York, 1972. [4] KUB´A ˇC EK, L.: Multivariate Statistical Models Revisited. Palack´y University, Olomouc, 2008. [5] KUB´A ˇC EK, L.: Seemingly Unrelated Regression Models, Appl. Math. (to appear). [6] KUB´A ˇC EK, L.-KUB´A ˇC KOV´A , L.-VOLAUFOV´A, J

Abstract

The paper investigates the effects of weave structure and fabric thread density on the comfort and mechanical properties of various test fabrics woven from polyester/cotton yarns. Three different weave structures, that is, 1/1 plain, 2/1 twill and 3/1 twill, and three different fabric densities were taken as input variables whereas air permeability, overall moisture management capacity, tensile strength and tear strength of fabrics were taken as response variables and a comparison is made of the effect of weave structure and fabric density on the response variables. The results of fabric samples were analysed in Minitab statistical software. The coefficients of determinations (R-sq values) of the regression equations show a good predictive ability of the developed statistical models. The findings of the study may be helpful in deciding appropriate manufacturing specifications of woven fabrics to attain specific comfort and mechanical properties.

., Kozintsev, I., Ramchandran, K., Moulin, P. (1999). Low complexity image denoising based on statistical modeling of wavelet coefficients. IEEE Signal Proc. Letters , 6, 300-303. Crouse, M. S., Nowak, R. D., Baraniuk, R. G. (1999). Analysis of multiresolution image denoising schemes using a generalized Gaussian and complexity priors. IEEE Trans. on Information Theory , 45, 909-919. Malfait, M., Roose, D. (1997). Wavelet-based image denoising using a markov random field a priori model. IEEE Trans. on Image Processing , 6, 549-565. Crouse, M. S., Nowak, R. D., Baraniuk, R

Abstract

The resilient behaviour of an unsaturated, unbound granular material is a primary input used in the mechanistic analysis of pavements incorporating such layers. Various models exist for the determination of the resilient behaviour, mainly based on the output of tri-axial laboratory testing. This paper describes an investigation where basic engineering properties such as grading, laboratory compaction characteristics and optimum moisture content are incorporated into the resilient behaviour model to quantify the effect of basic material properties on the resilient response of unsaturated, unbound granular materials. Such a resilient behaviour model will enable practitioners to estimate the behaviour of specific material, which might enable the use of available quality material that was discarded in the past. Data from tri-axial laboratory tests on materials originating from the Long Term Pavement Performance test sections are combined with basic engineering parameters of typical unbound granular material through a statistical modelling process to develop a model for predicting resilient behaviour, which can be used as a practical predictor of the expected behaviour during a Level 2 and/or Level 3 Mechanistic Empirical Pavement Design analysis. The work illustrates the process and the potential to develop a general resilient behaviour model for unbound granular materials incorporating saturation effects.