Transformed generalized linear models

The estimation of data transformation is very useful to yield response variables satisfying closely a normal linear model, Generalized linear models enable the fitting of models to a wide range of data types. These models are based on exponential dispersion models. We propose a new class of transformed generalized linear models to extend the Box and Cox models and the generalized linear models. We use the generalized linear model framework to fit these models and discuss maximum likelihood estimation and inference. We give a simple formula to estimate the parameter that index the transformation of the response variable for a subclass of models. We also give a simple formula to estimate the rth moment of the original dependent variable. We explore the possibility of using these models to time series data to extend the generalized autoregressive moving average models discussed by Benjamin er al. [Generalized autoregressive moving average models. J. Amer. Statist. Assoc. 98, 214-223]. The usefulness of these models is illustrated in a Simulation study and in applications to three real data sets. (C) 2009 Elsevier B.V. All rights reserved.

An Analysis of the Telecommunications Business in China by Linear Regression

In this paper, we study the influence of the National Telecom Business Volume by the data in 2008 that have been published in China Statistical Yearbook of Statistics. We illustrate the procedure of modeling “National Telecom Business Volume” on the following eight variables, GDP, Consumption Levels, Retail Sales of Social Consumer Goods Total Renovation Investment, the Local Telephone Exchange Capacity, Mobile Telephone Exchange Capacity, Mobile Phone End Users, and the Local Telephone End Users. The testing of heteroscedasticity and multicollinearity for model evaluation is included. We also consider AIC and BIC criterion to select independent variables, and conclude the result of the factors which are the optimal regression model for the amount of telecommunications business and the relation between independent variables and dependent variable. Based on the final results, we propose several recommendations about how to improve telecommunication services and promote the economic development.

Feasible estimation of generalized linear mixed models (GLMM) with weak dependency between groups

This paper presents a two-step pseudo likelihood estimation technique for generalized linear mixed models with the random effects being correlated between groups. The core idea is to deal with the intractable integrals in the likelihood function by multivariate Taylor's approximation. The accuracy of the estimation technique is assessed in a Monte-Carlo study. An application of it with a binary response variable is presented using a real data set on credit defaults from two Swedish banks. Thanks to the use of two-step estimation technique, the proposed algorithm outperforms conventional pseudo likelihood algorithms in terms of computational time.

Likelihood prediction for generalized linear mixed models under covariate uncertainty

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable generalized linear model, it has been shown that in complicated cases LP produces better results than already know methods.

Discovering linear causal model from incomplete data

One common drawback in algorithms for learning Linear Causal Models is that they can not deal with incomplete data set. This is unfortunate since many real problems involve missing data or even hidden variable. In this paper, based on multiple imputation, we propose a three-step process to learn linear causal models from incomplete data set. Experimental results indicate that this algorithm is better than the single imputation method (EM algorithm) and the simple list deletion method, and for lower missing rate, this algorithm can even find models better than the results from the greedy learning algorithm MLGS working in a complete data set. In addition, the method is amenable to parallel or distributed processing, which is an important characteristic for data mining in large data sets.

Target tracking with range and bearing measurements via robust linear filtering

In this paper we provide a robust version of a linear Kalman filter for target tracking with nonlinear range and bearing measurements. Moreover, we prove that the state estimation error is bounded in a probabilistic sense. We compare our approach with the current state of the art in converted radar measurement based linear filtering.

A state space based approach in non-linear hemodynamic response modeling with fMRI data

In this paper we use the modified and integrated version of the balloon model in the analysis of fMRI data. We propose a new state space model realization for this balloon model and represent it with the standard A,B,C and D matrices widely used in system theory. A second order Padé approximation with equal numerator and denominator degree is used for the time delay approximation in the modeling of the cerebral blood flow. The results obtained through numerical solutions showed that the new state space model realization is in close agreement to the actual modified and integrated version of the balloon model. This new system theoretic formulation is likely to open doors to a novel way of analyzing fMRI data with real time robust estimators. With further development and validation, the new model has the potential to devise a generalized measure to make a significant contribution to improve the diagnosis and treatment of clinical scenarios where the brain functioning get altered. Concepts from system theory can readily be used in the analysis of fMRI data and the subsequent synthesis of filters and estimators.

Recognition from appearance subspaces across image sets of variable scale

Linear subspace representations of appearance variation are pervasive in computer vision. In this paper we address the problem of robustly matching them (computing the similarity between them) when they correspond to sets of images of different (possibly greatly so) scales. We show that the naïve solution of projecting the low-scale subspace into the high-scale image space is inadequate, especially at large scale discrepancies. A successful approach is proposed instead. It consists of (i) an interpolated projection of the low-scale subspace into the high-scale space, which is followed by (ii) a rotation of this initial estimate within the bounds of the imposed “downsampling constraint”. The optimal rotation is found in the closed-form which best aligns the high-scale reconstruction of the low-scale subspace with the reference it is compared to. The proposed method is evaluated on the problem of matching sets of face appearances under varying illumination. In comparison to the naïve matching, our algorithm is shown to greatly increase the separation of between-class and within-class similarities, as well as produce far more meaningful modes of common appearance on which the match score is based.

Hydrograph separation and development of empirical relationships using single-parameter digital filters

A reliable hydrograph separation method is necessary for surface runoff modeling and hydrological studies. This paper investigates and compares the separation characteristics of two single-parameter digital filters, which are herein referred to as the one-parameter algorithm and the conceptual method. The application of the one-parameter algorithm was found to be restricted to low and medium baseflow separations, with a maximum separation limit of 50% of the total runoff hydrograph. The one-parameter algorithm was also observed to produce unrealistic sharp peaks under the peaks of the measured hydrograph when recession constant is smaller than 0.96. On the other hand, the conceptual method is applicable even for catchments fed largely by groundwater discharge. However, a reliable estimation of recession constant is a prerequisite for applying the conceptual method for large baseflow separations. Based on the hydrograph separation results, useful empirical relationships were developed for a partially urbanized watershed to estimate total runoff and direct runoff from the measured rainfall depth. The relationships between rainfall depth and total runoff depth and rainfall depth and direct runoff depth were found to be well represented by linear equations. The empirical relationships were then applied to estimate the long-term contribution of baseflow and surface runoff to total runoff at the study site. Baseflow was found to contribute about 58–61% of the annual total runoff.

New variable step-sizes minimizing mean-square deviation for the lms-type algorithms

The least-mean-square-type (LMS-type) algorithms are known as simple and effective adaptation algorithms. However, the LMS-type algorithms have a trade-off between the convergence rate and steady-state performance. In this paper, we investigate a new variable step-size approach to achieve fast convergence rate and low steady-state misadjustment. By approximating the optimal step-size that minimizes the mean-square deviation, we derive variable step-sizes for both the time-domain normalized LMS (NLMS) algorithm and the transform-domain LMS (TDLMS) algorithm. The proposed variable step-sizes are simple quotient forms of the filtered versions of the quadratic error and very effective for the NLMS and TDLMS algorithms. The computer simulations are demonstrated in the framework of adaptive system modeling. Superior performance is obtained compared to the existing popular variable step-size approaches of the NLMS and TDLMS algorithms. © 2014 Springer Science+Business Media New York.

Functional observer design with application to pre-compensated multi-variable systems

Partial state estimation of dynamical systems provides significant advantages in practical applications. Likewise, pre-compensator design for multi variable systems invokes considerable increase in the order of the original system. Hence, applying functional observer to pre-compensated systems can result in lower computational costs and more practicability in some applications such as fault diagnosis and output feedback control of these systems. In this note, functional observer design is investigated for pre-compensated systems. A lower order pre-compensator is designed based on a H2 norm optimization that is designed as the solution of a set of linear matrix inequalities (LMIs). Next, a minimum order functional observer is designed for the pre-compensated system. An LTI model of an irreversible chemical reactor is used to demonstrate our design algorithm, and to highlight the benefits of the proposed schemes.

Low cost BIST techniques for linear and non-linear analog circuits

With the ever increasing demands for high complexity consumer electronic products, market pressures demand faster product development and lower cost. SoCbased design can provide the required design flexibility and speed by allowing the use of IP cores. However, testing costs in the SoC environment can reach a substantial percent of the total production cost. Analog testing costs may dominate the total test cost, as testing of analog circuits usually require functional verification of the circuit and special testing procedures. For RF analog circuits commonly used in wireless applications, testing is further complicated because of the high frequencies involved. In summary, reducing analog test cost is of major importance in the electronic industry today. BIST techniques for analog circuits, though potentially able to solve the analog test cost problem, have some limitations. Some techniques are circuit dependent, requiring reconfiguration of the circuit being tested, and are generally not usable in RF circuits. In the SoC environment, as processing and memory resources are available, they could be used in the test. However, the overhead for adding additional AD and DA converters may be too costly for most systems, and analog routing of signals may not be feasible and may introduce signal distortion. In this work a simple and low cost digitizer is used instead of an ADC in order to enable analog testing strategies to be implemented in a SoC environment. Thanks to the low analog area overhead of the converter, multiple analog test points can be observed and specific analog test strategies can be enabled. As the digitizer is always connected to the analog test point, it is not necessary to include muxes and switches that would degrade the signal path. For RF analog circuits, this is specially useful, as the circuit impedance is fixed and the influence of the digitizer can be accounted for in the design phase. Thanks to the simplicity of the converter, it is able to reach higher frequencies, and enables the implementation of low cost RF test strategies. The digitizer has been applied successfully in the testing of both low frequency and RF analog circuits. Also, as testing is based on frequency-domain characteristics, nonlinear characteristics like intermodulation products can also be evaluated. Specifically, practical results were obtained for prototyped base band filters and a 100MHz mixer. The application of the converter for noise figure evaluation was also addressed, and experimental results for low frequency amplifiers using conventional opamps were obtained. The proposed method is able to enhance the testability of current mixed-signal designs, being suitable for the SoC environment used in many industrial products nowadays.

Câmbio real do Brasil : determinantes de longo prazo: evidências a partir de testes não paramétricos de cointegração e de tendência não linear comum

O trabalho procura investigar a existência de relação de cointegração entre a Taxa de Câmbio Real (CRER), Passivo Externo Líquido (PEL), Termos de Troca (TOT) e um fator de produtividade (BS), utilizando um teste não paramétrico proposto por Bierens (1997), aplicado a uma amostra de dados para EUA e Brasil que cobre o período de 1980 a 2010. Para os EUA, é encontrada evidência da influência das variáveis elencadas. No caso brasileiro verifica-se pouca relevância da variável BS, sendo as demais variáveis presentes no vetor de cointegração.

Generalized linear mixed models for the genetic evaluation of binary reproductive traits: a simulation study

The objective of this study was to evaluate the use of probit and logit link functions for the genetic evaluation of early pregnancy using simulated data. The following simulation/analysis structures were constructed: logit/logit, logit/probit, probit/logit, and probit/probit. The percentages of precocious females were 5, 10, 15, 20, 25 and 30% and were adjusted based on a change in the mean of the latent variable. The parametric heritability (h²) was 0.40. Simulation and genetic evaluation were implemented in the R software. Heritability estimates (ĥ²) were compared with h² using the mean squared error. Pearson correlations between predicted and true breeding values and the percentage of coincidence between true and predicted ranking, considering the 10% of bulls with the highest breeding values (TOP10) were calculated. The mean ĥ² values were under- and overestimated for all percentages of precocious females when logit/probit and probit/logit models used. In addition, the mean squared errors of these models were high when compared with those obtained with the probit/probit and logit/logit models. Considering ĥ², probit/probit and logit/logit were also superior to logit/probit and probit/logit, providing values close to the parametric heritability. Logit/probit and probit/logit presented low Pearson correlations, whereas the correlations obtained with probit/probit and logit/logit ranged from moderate to high. With respect to the TOP10 bulls, logit/probit and probit/logit presented much lower percentages than probit/probit and logit/logit. The genetic parameter estimates and predictions of breeding values of the animals obtained with the logit/logit and probit/probit models were similar. In contrast, the results obtained with probit/logit and logit/probit were not satisfactory. There is need to compare the estimation and prediction ability of logit and probit link functions.

Distribuição da carga hidráulica em linhas de derivação otimizadas por programação linear

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)