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The article discusses geometric mean criterion and stochastic dominance decision making models. The author derives the mathematical relationship between the geometric mean and the integrals of the probability density functions used in stochastic dominance testing from a general class of distributions. However, both the models can be justified by the expected utility hypothesis. The geometric mean criterion follows as a result of the assumption that the decision-maker has a logarithmic utility function. The stochastic dominance models require the assumption of the signs of the first few derivatives of the decision-maker's utility function. The author notes that stochastic dominance showed great promise in its early development as a method for constructing a theory of security price forecasting because of its limited set of assumptions regarding investor preferences.

Forecasting for nonlinear time series is an important topic in time series analysis. Existing numerical algorithms for multi-step-ahead forecasting ignore accuracy checking, alternative Monte Carlo methods are also computationally very demanding and their accuracy is difficult to control too. In this paper a numerical forecasting procedure for nonlinear autoregressive time series models is proposed. The forecasting procedure can be used to obtain approximate m-step-ahead predictive probability density functions, predictive distribution functions, predictive mean and variance, etc. for a range of nonlinear autoregressive time series models. Examples in the paper show that the forecasting procedure works very well both in terms of the accuracy of the results and in the ability to deal with different nonlinear autoregressive time series models. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper explores the performance of the local splitting criterion devised by Bremner&Taplin for classification and regression trees when multiple trees are averaged to improve performance. The criterion is compared with the deviance used by Clark&Pregibon's method, which is a global splitting criterion typically used to grow trees. The paper considers multiple trees generated by randomly selecting splits with probability proportional to the likelihood for the split, and by bagging where bootstrap samples from the data are used to grow trees. The superiority of the localized splitting criterion often persists when multiple trees are grown and averaged for six datasets.Tree averaging is known to be advantageous when the trees being averaged produce different predictions, and this can be achieved by choosing splits where the splitting criterion is locally optimal. The paper shows that use of locally optimal splits gives promising results in conjunction with both local and global splitting criteria, and with and without random selection of splits. The paper also extends the local splitting criterion to accommodate categorical predictors. [ABSTRACT FROM AUTHOR]

With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods. [ABSTRACT FROM AUTHOR]

The purposes of this paper are to introduce a multivariate non-stationary stochastic time series model without individual detrending and to extract the multiple relationships between variables. To infer the statistical relation between variables, we attempt to estimate the co-movement of multivariate non- stationary time series components. The model is expressed in state-space form, and time series components are estimated by the maximum likelihood method using numerical optimization algorithm. The Kalman filter algorithm is used to compute the likelihood of the model. The AIC procedure gives a criterion for selecting the best model fit for the data. The multiple relationship becomes clear by analysing estimated AR coefficients. Real economic data are used for a numerical example. [ABSTRACT FROM AUTHOR]

Beautiful formulas are knownfor the expected cost of random two-dimensional assignment problems, but in higher dimensions even the scaling is not known. In three dimensions and above, the problem has natural "Axial" and "Planar" versions, both of which are NP-hard. For 3-dimensional Axial random assignment instances of size n, the cost scales as Ω(1/n), and a main result of the present paper is a linear-time algorithm that, with high probability, finds a solution of cost O(n-1+o(1)). For 3- dimensional Planar assignment, the lower bound is Ω(n), and we give a new efficient matching-based algorithm that with high probability returns a solution with cost O(n log n). [ABSTRACT FROM AUTHOR]

Kohomen's model as the self-organizing model for the neural network can be considered as a kind of adaptive vector quantization algorithm. Numerous reports have been presented on the application of the model to practical problems. Although some results have been presented for the theoretical properties of Kohonen's model, many properties remain to be clarified. Among various properties of Kohonen's model as an adaptive vector quantization algorithm, this paper considers the problem of how the reference vectors are placed according to the probability distribution of the input signal. Considering the limit where the number of reference vectors is increased to infinity, this problem can be discussed theoretically as the distribution of the reference vectors. Due to the effect of the "learning by neighborhood," which is the feature of Kohonen's model, the property of the Kohonen model differs quantitatively from the property of the ordinary vector quantization algorithm. This paper discusses quantitatively the properties of Kohonen's model using the average learning equation. [ABSTRACT FROM AUTHOR]

Background: Item response times in computerized assessments are frequently used to identify rapid guessing behaviour as a manifestation of response disengagement. However, non‐rapid responses (i.e., with longer response times) are not necessarily engaged, which means that response‐time‐based procedures could overlook disengaged responses. Therefore, the identification of disengaged responses could be improved by considering additional indicators of disengagement. We investigated the extent to which decreases in individuals' item solution probabilities over the course of a test reflect disengaged response behaviour. Objectives: To disentangle different types of possibly disengaged responses and better understand non‐effortful test‐taking behaviour, we augmented responses‐time‐based procedures for identifying rapid guessing with strategies for detecting disengaged responses on the basis of performance declines in non‐rapid responses. Methods: We combined item response theory (IRT) models for rapid guessing and test‐taking persistence to examine the capability of response times and item positions to capture response disengagement. We used a computerized assessment in which science items were randomly distributed across positions for each student. This allowed us to estimate individual differences in test‐taking persistence (i.e., the duration for which the initial level of performance is maintained) while accounting for rapid responses. Results and Conclusions: Response times did not fully explain disengagement; item responses reflected test‐taking persistence even when rapid responses were accounted for. This interpretation was supported by a strong correlation of test‐taking persistence with decreases in self‐reported test‐taking effort. Furthermore, our results suggest that IRT models for test‐taking persistence can effectively account for the undesirable impact of low test‐taking effort even when response times are unavailable. Practitioner Notes: Assessments of proficiencies that attempt to quantify what individuals know and can do lead to biased results when individuals provide disengaged responses.Item response times are frequently used to identify rapid guessing behaviour as a manifestation of response disengagement, but response‐time‐based procedures could overlook disengaged responses.To disentangle different types of possibly disengaged responses and better understand non‐effortful test‐taking behaviour, we augmented responses‐time‐based procedures for identifying rapid guessing with strategies for detecting disengaged responses on the basis of performance declines in non‐rapid responses.In a sample of fifth and sixth graders, we found that response times did not fully explain disengagement, as many students showed performance declines in non‐rapid item responses.Our results suggest that item response theory models for test‐taking persistence can effectively account for the undesirable impact of low test‐taking effort even when response times are unavailable. [ABSTRACT FROM AUTHOR]

The article demonstrates the connection between switching regressions and logistic discrimination. It provides an alternative, intuitive, motivation for the Behboodiah-Hartley algorithm for numerically solving the likelihood equations. The argument gives an interpretation of the maximum likelihood estimator of the switching regression model as a weighted regression estimator with logit probabilities as weights. The results of the paper suggest that the classification of the observations could be done on the basis of the estimated logistic probabilities, conditioning on the estimated value of the parameters.

Models based on the Student's t distribution are proposed with its scale parameter randomized. Mathematical properties of the models such as their probability density functions, cumulative distribution functions, moments and characteristic functions are derived. Three of the models are fitted to daily log returns of six financial indices. They were shown to provide better fits than mixtures of Student's t distributions and the popular generalized hyperbolic distribution. [ABSTRACT FROM AUTHOR]

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Despite high degrees of uncertainty associated with graphical functions, sensitivity analysis of these functions has received less attention than parametric sensitivity analysis. Recently, a promising method was proposed in the literature to generate alternative functional forms, reducing the problem to that of parametric sensitivity. Yet the usability of the method for graphical functions in system dynamics has not been investigated. We apply this function distortion method to a sample model and identify a number of shortcomings, such as a limited variety of alternative forms. We then propose extensions to the method to address the shortcomings, and subsequently test the extensions. We find the (extended) method of function distortion to be readily applicable and efficient in testing the sensitivity of model outputs to variations in the form of graphical functions. The proposed extensions increase the variety of possible distortions, but further research can be conducted on the control of the distortions. Copyright © 2014 System Dynamics Society [ABSTRACT FROM AUTHOR]

This article introduces an agglomerative Bayesian model-based clustering algorithm which outputs a nested sequence of two-way cluster configurations for an input matrix of data. Each two-way cluster configuration in the output hierarchy is specified by a row configuration and a column configuration whose Cartesian product partitions the data matrix. Variable selection is incorporated into the algorithm by identifying row clusters which form distinct groups defined by the column clusters, through the use of a mixture model. A primitive similarity measure between the two clusters is the multiplicative change in model posterior probability implied by their merger, and the hierarchy is formed by iteratively merging the cluster pair which maximize some fixed monotonic function of this quantity. A naive implementation of the algorithm would be to choose this function to be the identity function. However, when applying this naive algorithm to gene expression data where the number of genes being studied typically far exceeds the number of experimental samples available, this imbalanced dimensionality of the data results in an algorithmic bias toward merging samples. To counteract this bias, alternative functions of the similarity measure are considered which prevent degenerative behavior of the algorithm. The resulting improvements in the output cluster configurations are demonstrated on simulated data and the method is then applied to real gene expression data. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012 [ABSTRACT FROM AUTHOR]

. Deterministic Bayesian inference for latent Gaussian models has recently become available using integrated nested Laplace approximations (INLA). Applying the INLA-methodology, marginal estimates for elements of the latent field can be computed efficiently, providing relevant summary statistics like posterior means, variances and pointwise credible intervals. In this article, we extend the use of INLA to joint inference and present an algorithm to derive analytical simultaneous credible bands for subsets of the latent field. The algorithm is based on approximating the joint distribution of the subsets by multivariate Gaussian mixtures. Additionally, we present a saddlepoint approximation to compute Bayesian contour probabilities, representing the posterior support of fixed parameter vectors of interest. We perform a simulation study and apply the given methods to two real examples. [ABSTRACT FROM AUTHOR]

Purpose: To investigate differences in calculated doses and normal tissue complication probability (NTCP) values between different dose algorithms. Methods: Six dose algorithms from four different treatment planning systems were investigated: Eclipse AAA, Oncentra MasterPlan Collapsed Cone and Pencil Beam, Pinnacle Collapsed Cone and XiO Multigrid Superposition, and Fast Fourier Transform Convolution. Twenty NSCLC patients treated in the period 2001-2006 at the same accelerator were included and the accelerator used for treatments were modeled in the different systems. The treatment plans were recalculated with the same number of monitor units and beam arrangements across the dose algorithms. Dose volume histograms of the GTV, PTV, combined lungs (excluding the GTV), and heart were exported and evaluated. NTCP values for heart and lungs were calculated using the relative seriality model and the LKB model, respectively. Furthermore, NTCP for the lungs were calculated from two different model parameter sets. Calculations and evaluations were performed both including and excluding density corrections. Results: There are found statistical significant differences between the calculated dose to heart, lung, and targets across the algorithms. Mean lung dose and V20 are not very sensitive to change between the investigated dose calculation algorithms. However, the different dose levels for the PTV averaged over the patient population are varying up to 11%. The predicted NTCP values for pneumonitis vary between 0.20 and 0.24 or 0.35 and 0.48 across the investigated dose algorithms depending on the chosen model parameter set. The influence of the use of density correction in the dose calculation on the predicted NTCP values depends on the specific dose calculation algorithm and the model parameter set. For fixed values of these, the changes in NTCP can be up to 45%. Conclusions: Calculated NTCP values for pneumonitis are more sensitive to the choice of algorithm than mean lung dose and V20 which are also commonly used for plan evaluation. The NTCP values for heart complication are, in this study, not very sensitive to the choice of algorithm. Dose calculations based on density corrections result in quite different NTCP values than calculations without density corrections. It is therefore important when working with NTCP planning to use NTCP parameter values based on calculations and treatments similar to those for which the NTCP is of interest. [ABSTRACT FROM AUTHOR]