In this paper we investigate the construction of combination functions in identification systems. In contrast to verification systems, the optimal combination functions for identification systems are not known. In this paper we represent the combination function by means of a neural network and explore different methods of its training, so that the identification system performance is optimized...

In regression analysis of count data, independent variables are often modeled by their linear effects under the assumption of log-linearity. In reality, the validity of such an assumption is rarely tested, and its use is at times unjustifiable. A lack-of-fit test is proposed for the adequacy of a postulated functional form of an independent variable within the framework of semiparametric Poisso...

The molecular-replacement method works well with good models and simple unit cells, but often fails with more difficult problems. Experience with likelihood in other areas of crystallography suggests that it would improve performance significantly. For molecular replacement, the form of the required likelihood function depends on whether there is ambiguity in the relative phases of the contribu...

A likelihood function based on the multivariate probability distribution of all observed structure-factor amplitudes from a single isomorphous replacement with anomalous scattering experiment has been derived and implemented for use in substructure refinement and phasing as well as macromolecular model refinement. Efficient calculation of a multidimensional integration required for function eva...

In this paper, we study the detection boundary for minimax hypothesis testing in the context of high-dimensional, sparse binary regression models. Motivated by genetic sequencing association studies for rare variant effects, we investigate the complexity of the hypothesis testing problem when the design matrix is sparse. We observe a new phenomenon in the behavior of detection boundary which do...

If the log likelihood is approximately quadratic with constant Hessian, then the maximum likelihood estimator (MLE) is approximately normally distributed. No other assumptions are required. We do not need independent and identically distributed data. We do not need the law of large numbers (LLN) or the central limit theorem (CLT). We do not need sample size going to infinity or anything going t...

" Cluster PIN: A new estimation method for the probability of informed trading " Abstract We present a new method for estimating the probability of informed trading (PIN). This method, called Cluster PIN (CPIN), is based on cluster analysis used in machine learning. CPIN does not require maximum likelihood estimation and thus avoids the computational issues that have been associated with some p...

This paper studies algorithms for reducing the computational e ort of the mixture density calculations in HMM-based speech recognition systems. These likelihood calculations take about 70 85% of the total recognition time in the RWTH system for large vocabulary continuous speech recognition. To reduce the computational cost of the likelihood calculations, we investigate several space partitioni...