Application of Advanced Statistical Procedures for Adjustment of Results in Measurements of Displacements

Abstract

In this paper, the authors verified the formulated principles of the estimation of Gauss-Markov models in which estimated parameters X were random. For this purpose, methods for the prior definition of covariance matrix C X for the estimated parameters were provided, which were used to determine the conditional covariance matrix of observation vector L and then estimate the most probable values of parameters X . Covariance matrix Cov(X) obtained as a result of this estimation was used to define the limit values of the variance of these parameters. Practical application of the proposed method for the Gauss-Markov model estimation for random parameters was illustrated on a fragment of a leveling network of points to determine the vertical displacements of a landslide surface.

The Application of Spatial Autoregressive Models for Analyzing the Influence of Spatial Factors on Real Estate Prices and Values

“…Spatial factors play a very important role in the analyses of the real estate market, but they are not easy to incorporate in models of the evaluated space. In the literature, most market analyses have been conducted with the use of multiple regression models and their derivatives (Isakson, 1998;Czaja, 2001;Benjamin et al, 2004;Sirmans et al, 2005;Adamczewski, 2006;Bitner, 2007;Czaja & Dąbrowski, 2008;Barańska, 2010;Sawiłow, 2010;Dąbrowski, 2011). Multiple regression models are a highly useful tool for analyzing transaction prices, but they are not frequently applied in practice, mainly due to problems with meeting formal requirements during model design (Hozer, 2001).…”