Matrix models disadvantages

Disadvantages of Matrix Models Compared to Other Models.39... 

DISADVANTAGES OF MATRIX MODELS COMPARED TO OTHER MODELS... 

One of the major disadvantages of matrix models is that they are based on present conditions (constant parameters) and project what might occur in the future based on these conditions (Banks et al. 2008). However, demographic and environmental parameters change through time. For example, reproductive and mortality rates often... 

Another disadvantage of matrix models is that the data necessary to develop a model are more difficult to obtain than acute mortality data or single measures of chronic effects on reproduction. 

Matrix models have a long history of use by conservation biologists and applied ecologists. They are easy to use and understand, and the data necessary for these models can be developed in the laboratory and/or the field. There are disadvantages to the use of matrix models the most important of these is that they are based on initial conditions and project what might occur based on these conditions. Thus, matrix models are projection, not prediction, models. Because matrix models can... 

The common disadvantage of both the free volume and configuration entropy models is their quasi-thermodynamic approach. The ion transport is better described on a microscopic level in terms of ion size, charge, and interactions with other ions and the host matrix. This makes a basis of the percolation theory, which describes formally the ion conductor as a random mixture of conductive islands (concentration c) interconnected by an essentially non-conductive matrix. (The mentioned formalism is applicable not only for ion conductors, but also for any insulator/conductor mixtures.)... 

As a surface analytical tool, SIMS has several advantages over X-ray photoelectron spectroscopy (XPS) and Auger electron spectroscopy (AES). SIMS is sensitive to all elements and isotopes in the periodic table, whereas XPS and AES cannot detect H and He. SIMS also has a lower detection limit of 10 5 atomic percent (at.S) compared to 0.1 at.S and 1.0 at.% for AES and XPS, respectively. However, SIMS has several disadvantages. Its elemental sensitivity varies over five orders of magnitude and differs for a given element in different sample matrices, i.e., SIMS shows a strong matrix effect. This matrix effect makes SIMS measurements difficult to quantify. Recent progress, however, has been made especially in the development of quantitative models for the analysis of semiconductors [3-5]. 

The impacts resulting from each scenario and the advantages and disadvantages of each measure (alternative) have to be estimated by computing tlie values the indicators take for each alternative. The estimation is done using tlie model calibrated during the previous step. At the end of this step an impact matrix is produced whose elements are the indicator values for each scenario and alternative (see Figure 4.2.4). This serves evaluation and comparison of results. 

A fundamental issue in the description of even the simplest, isolated autoionising resonance in the parametric approach followed by Fano [391] - and further pursued in K-matrix theory - is that the atom cannot be deperturbed, that is one cannot access the so-called prediagonalised states which are imagined to exist prior to autoionisation being included as a perturbative interaction, since the effect is anyway internal to the atom and cannot truly be turned off. This has the disadvantage that the parameters, once they have been obtained, must still be calculated from an ab initio model of the atom for a full comparison with theory. It might seem that the parametric theory cannot really be checked independently of ab initio calculations whose accuracy is hard to ascertain. 

A disadvantage of this calibration method is the fact that the calibration coefficients (elements of the P matrix) have no physical meaning, since they do not reflect the spectra of the individual components. The usual assumptions about errorless independent variables (here, the absorbances) and error-prone dependent variables (here, concentrations) are not valid. Therefore, if this method of inverse calibration is used in coimection with OLS for estimating the P coefficients, there is only a slight advantage over the classical /C-matrix approach, due to the fact that a second matrix inversion is avoided. However, in coimection with more soft modeling methods, such as PCR or PLS, the inverse calibration approach is one of the most frequently used calibration tools. 

The disadvantages of the model include the fact that it depends on the selected historical transition matrix. The applicability of this matrix to future periods needs to be considered carefully, whether, for example, it adequately describes future credit migration patterns. In addition it assumes all securities with the same credit rating have the same spread, which is restrictive. For this reason the spread levels chosen in the model are a key assumption in the pricing model. Finally, the constant recovery rate is another practical constraint as in practice, the level of recovery will vary. 

---