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This paper develops object-oriented data analysis (OODA) statistical methods that are novel and complementary to existing methods of analysis of human brain scan connectomes, defined as graphs representing brain anatomical or functional connectivity. OODA is an emerging field where classical statistical approaches (e.g., hypothesis testing, regression, estimation, and confidence intervals) are applied to data objects such as graphs or functions. By analyzing data objects directly we avoid loss of information that occurs when data objects are transformed into numerical summary statistics. By providing statistical tools that analyze sets of connectomes without loss of information, new insights into neurology and medicine may be achieved. In this paper we derive the formula for statistical model fitting, regression, and mixture models; test their performance in simulation experiments; and apply them to connectomes from fMRI brain scans collected during a serial reaction time task study. Software for fitting graphical object-oriented data analysis is provided. [ABSTRACT FROM AUTHOR]

In clinical trials and biomedical studies, treatments are compared to determine which one is effective against illness; however, individuals can react to the same treatment very differently. We propose a complete process for longitudinal data that identifies subgroups of the population that would benefit from a specific treatment. A random effects linear model is used to evaluate individual treatment effects longitudinally where the random effects identify a positive or negative reaction to the treatment over time. With the individual treatment effects and characteristics of the patients, various classification algorithms are applied to build prediction models for subgrouping. While many subgrouping approaches have been developed recently, most of them do not check its validity. In this paper, we further propose a simple validation approach which not only determines if the subgroups used are appropriate and beneficial but also compares methods to predict individual treatment effects. This entire procedure is readily implemented by existing packages in statistical software. The effectiveness of the proposed method is confirmed with simulation studies and analysis of data from the Women Entering Care study on depression. [ABSTRACT FROM AUTHOR]

In this paper, a formulation of the convergence property of the NLMS method, an updating algorithm for an adaptive Volterra filter, is discussed. To date, much research has been carried out on the convergence property of the updating algorithm in a linear adaptive filter. However, there has been insufficient study of the updating algorithm for the second-order adaptive Volterra filter. In this paper, the convergence property of the E-NLMS method, an updating algorithm in the second-order adaptive Volterra filter, is formulated and the convergence property of the second-order adaptive Volterra filter is theoretically determined. In the formulation of the convergence property we use the first-order recursive filter expression, which is effective for studying various characteristics of the NLMS method in linear adaptive filters. In addition, it is recognized that the nature of the input signal to the diagonal (D) elements of the second-order Volterra system is different from that of the input to the other (ED) components. By means of computer simulation, it is confirmed that the convergence property given by the derived theoretical equation can sufficiently express the actual convergence property. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 85(10): 40–50, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.1124 [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, a reliability-based computational algorithm was developed and coded into a computer program, P-UASLOPE, for design of a row of equally spaced drilled shafts to stabilize an unstable slope while achieving the required target reliability index with minimum volume of drilled shafts. The Monte Carlo simulation technique was used in the previously developed deterministic computational program, in which the limiting equilibrium method of slices was modified to incorporate the arching effects of the drilled shafts in a slope. Uncertainties of soil parameters in the slope were considered by statistical descriptors, including mean, coefficient of variance, and distribution function. Model errors of the semi-empirical predictive equation for the load transfer factor for characterizing the soil arching effects were considered by statistics of bias. A total of 41 cases of 3-D finite element simulation results were used to determine the statistics of bias. Two design examples, one is a two-layer slope and the other one is an actual failed slope, were given to demonstrate the use of P-UASLOPE program for optimized design of drilled shafts reinforced slope system to achieve the most economic combination of design variables (i.e., location, spacing, diameter, and length of drilled shafts) to satisfy the design requirements in terms of target reliability index and the structural performance of the drilled shafts. A single value of factor of safety chosen in the deterministic approach may not yield the desired level of reliability as uncertainties of soil parameters and model errors cannot be accounted for systematically. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The paper presents an algorithm for calculating the three-dimensional Voronoi–Delaunay tessellation for an ensemble of spheres of different radii (additively-weighted Voronoi diagram). Data structure and output of the algorithm is oriented toward the exploration of the voids between the spheres. The main geometric construct that we develop is the Voronoi S-network (the network of vertices and edges of the Voronoi regions determined in relation to the surfaces of the spheres). General scheme of the algorithm and the key points of its realization are discussed. The principle of the algorithm is that for each determined site of the network we find its neighbor sites. Thus, starting from a known site of the network, we sequentially find the whole network. The starting site of the network is easily determined based on certain considerations. Geometric properties of ensembles of spheres of different radii are discussed, the conditions of applicability and limitations of the algorithm are indicated. The algorithm is capable of working with a wide variety of physical models, which may be represented as sets of spheres, including computer models of complex molecular systems. Emphasis was placed on the issue of increasing the efficiency of algorithm to work with large models (tens of thousands of atoms). It was demonstrated that the experimental CPU time increases linearly with the number of atoms in the system, O(n). © 2006 Wiley Periodicals, Inc. J Comput Chem, 2006 [ABSTRACT FROM AUTHOR]

The low pass filter (LPF) algorithm, which is used to calculate the average queue length in the random early detection (RED) algorithm, results in a poor response time when RED recovers from congestion. In this paper, we propose a new algorithm, called low pass filter/over drop avoidance (LPF/ODA), to improve the response time of RED. LPF/ODA is based on reducing the time required by RED to recover from long-term congestion. Simulation results have been used to show that the proposed LPF/ODA algorithm significantly improves the response time, throughput and delay of RED gateways. Copyright © 2002 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive array is proposed in which the CMA (Constant Modulus Algorithm) and LMS algorithms are combined so that a pass diversity effect is obtained without using training signals. The basic operations of the proposed algorithm are as follows. CMA captures the direct wave with maximum power. Based on the output of the CMA adaptive filter, a reference signal is generated. The LMS algorithm, using this reference signal, captures the delayed wave. By combining the direct wave and the delayed wave at maximum rate, the receiving quality is improved. By means of a computer simulation, we confirmed that the convergence property is improved and that the effect of the reference signal error on the system convergence characteristics is small. © 1998 Scripta Technica. Electron Comm Jpn Pt 3, 81(7): 37–44, 1998 [ABSTRACT FROM AUTHOR]

Two methods have been proposed for calculating the block orthogonal projection algorithm using the Moore-Penrose generalized inverse matrix. One is the conjugate gradient method, and the other is the block adaptive algorithm composed of the Gram-Schmidt orthogonalization and the learning identification. Both computation methods are suited to the high-speed estimation of the optimal coefficients with a small computational complexity. However, the operation may become very unstable due to the effects of observation error, quantization error, and other factors. This paper proposes a method of realizing the block orthogonal projection algorithm with the learning identification, where the orthogonal basis of the space spun by the state matrix in the block adaptive filter is used as the direction of modification. A feature of this method is that the orthogonal basis is calculated using the orthogonal projection matrix. Computer simulation shows that the proposed adaptive algorithm realizes a very stable operation without being affected by either the quantization error, the operational error, or the observation noise. [ABSTRACT FROM AUTHOR]