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The key variables for (credit) risk assessment are the probability of default (PD), the loss given default (LGD) and the exposure at default (EAD).The credit conversion factor calculates the amount of a free credit line and other off-balance-sheet transactions (with the exception of derivatives) to an EAD amount [2] and is an integral part in the European banking regulation since the Basel II ...
In ()-(), L1-norm ‖ ‖ returns the sum of the absolute entries of its argument and L2-norm ‖ ‖ returns the sum of the squared entries of its argument.If one substitutes ‖ ‖ in by the Frobenius/L2-norm ‖ ‖, then the problem becomes standard PCA and it is solved by the matrix that contains the dominant singular vectors of (i.e., the singular vectors that correspond to the highest ...
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.
Scoring algorithm, also known as Fisher's scoring, [1] is a form of Newton's method used in statistics to solve maximum likelihood equations numerically, named after Ronald Fisher. Sketch of derivation
Since the balance sheet is founded on the principles of the accounting equation, this equation can also be said to be responsible for estimating the net worth of an entire company. The fundamental components of the accounting equation include the calculation of both company holdings and company debts; thus, it allows owners to gauge the total ...
Each value of k determines what is known as a branch (or sheet), a single-valued component of the multiple-valued log function. When the focus is on a single branch, sometimes a branch cut is used; in this case removing the non-positive real numbers from the domain of the function and eliminating π {\displaystyle \pi } as a possible value for ...
One approach is to start with a model in general form that relies on a theoretical understanding of the data-generating process. Then the model can be fit to the data and checked for the various sources of misspecification, in a task called statistical model validation .
As another example, suppose that the data consists of points (x, y) that we assume are distributed according to a straight line with i.i.d. Gaussian residuals (with zero mean): this leads to the same statistical model as was used in the example with children's heights. The dimension of the statistical model is 3: the intercept of the line, the ...