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Time series of vegetation indices (VI) derived from satellite imagery provide a consistent monitoring system for terrestrial plant productivity. They enable detection and quantification of gradual changes within the time frame covered, which are of crucial importance in global change studies, for example. However, VI time series typically contain a strong seasonal signal which complicates change detection. Commonly, trends are quantified using linear regression methods, while the effect of serial autocorrelation is remediated by temporal aggregation over bins having a fixed width. Aggregating the data in this way produces temporal units which are modifiable. Analogous to the well-known Modifiable Area Unit Problem (MAUP), the way in which these temporal units are defined may influence the fitted model parameters and therefore the amount of change detected. This paper illustrates the effect of this Modifiable Temporal Unit Problem (MTUP) on a synthetic data set and a real VI data set. Large variation in detected changes was found for aggregation over bins that mismatched full lengths of vegetative cycles, which demonstrates that aperiodicity in the data may influence model results. Using 26 yr of VI data and aggregation over full-length periods, deviations in VI gains of less than 1% were found for annual periods (with respect to seasonally adjusted data), while deviations increased up to 24% for aggregation windows of 5 yr. This demonstrates that temporal aggregation needs to be carried out with care in order to avoid spurious model results. [ABSTRACT FROM AUTHOR]

It is the common lore to assume that knowing the equation for the probability distribution function (PDF) of a stochastic model as a function of time tells the whole picture defining all other characteristics of the model. We show that this is not the case by comparing two exactly solvable models of anomalous diffusion due to geometric constraints: the comb model and the random walk on a random walk. We show that though the two models have exactly the same PDFs, they differ in other respects, like their first passage time distributions, their autocorrelation functions, and their aging properties. [ABSTRACT FROM AUTHOR]

A high-resolution or complete bathymetric map of shallow water based on sparse point measurements (depth soundings) is often needed. One possible approach to such maps is passive remote sensing of water depth by using multispectral imagery in the popular method proposed by Lyzenga et al. [2006]; however, its application has been limited due to insufficient accuracy. To improve accuracy, we have developed 3 extensions of Lyzenga's method by addressing unrealistic optical and statistical assumptions in the method. The purpose of this paper is to compare the accuracy of Lyzenga's method, the 3 extensions, and the combination of the 3 extensions. The accuracy comparison test was performed for 2 coral reef sites by using cross validation. The results indicated that for both sites, the extended methods were more accurate than Lyzenga's method when sufficient training data were available. The most accurate extension was the one derived by modeling the spatial autocorrelation in the error term of the regression model used in Lyzenga's method. The combination of the 3 extensions was even more accurate than the extensions. The implementations of the extended methods are not difficult in terms of software availability and computational cost. [ABSTRACT FROM AUTHOR]

Abstract: Insufficient attention has been given to hotel-room-price attributions and its mechanism in the lodging research field till now. This article examines how site and situation factors differently affect lodging industry and room prices. Comparative analysis of four hedonic price models has been conducted to investigate how these attributions influence room price of Beijing''s hotels above star three. Spatial autocorrelation in hotel prices and in hedonic room price equation residuals were analyzed in this research too. Some conclusions can be found and summarized: according to the estimated results, for specific locales, the results expressed in a global model might be inaccurate. The fitting coefficient of geographically weighted regression demonstrates the importance of going beyond the global modeling framework when incorporating geographically weighted regression into hedonic price model. At last, an innovative method for determining the influence of a hotel''s attributes at market rates on its’ values, or implicit prices was put forward by the authors. [Copyright &y& Elsevier]

We demonstrate that t ratios (the F statistic) for I(1) regressors in a model with an I(0) dependent variable will generally be oversized. This indicates that spurious significance occurs in a situation where it was not previously identified. We also compare the asymptotic rejection rates of t ratios for various combinations of I(1) and I(0) variables in the two-variable linear regression model. These rejection rates systematically increase with the degree of autocorrelation, yielding spurious significance, when both variables are either positively or negatively autocorrelated. In contrast, when one variable is negatively autocorrelated and the other is positively autocorrelated the rejection rates systematically fall and are undersized. [ABSTRACT FROM AUTHOR]

In this paper, we compare by means of Monte Carlo simulations two approaches to take spatial autocorrelation into account: the classical spatial autoregressive model and the structural equations model with latent variables. The former accounts for spatial dependence and spillover effects in georeferenced data by means of a spatial weights matrix W. The latter represents spatial dependence and spillover effects by means of a latent variable in the structural (regression) model while the observed spatially lagged variables are related to the latent spatial dependence variable in the measurement model. The simulation results based on Anselin's Columbus, Ohio, crime data set show that the misspecified latent variables approach slightly trails the correctly specified classical approach in terms of bias and root mean squared error of the coefficient estimators. [ABSTRACT FROM AUTHOR]

Abstract: We propose third-order likelihood-based methods to derive highly accurate p-value approximations for testing autocorrelated disturbances in nonlinear regression models. The proposed methods are particularly accurate for small- and medium-sized samples whereas commonly used first-order methods like the signed log-likelihood ratio test, the test, and the standardized test can be seriously misleading in these cases. Two Monte Carlo simulations are provided to show how the proposed methods outperform the above first-order methods. An empirical example applied to US population census data is also provided to illustrate the implementation of the proposed method and its usefulness in practice. [Copyright &y& Elsevier]

Abstract: A Bayesian method for estimating a time-varying regression model subject to the presence of structural breaks is proposed. Heteroskedastic dynamics, via both GARCH and stochastic volatility specifications, and an autoregressive factor, subject to breaks, are added to generalize the standard return prediction model, in order to efficiently estimate and examine the relationship and how it changes over time. A Bayesian computational method is employed to identify the locations of structural breaks, and for estimation and inference, simultaneously accounting for heteroskedasticity and autocorrelation. The proposed methods are illustrated using simulated data. Then, an empirical study of the Taiwan and Hong Kong stock markets, using oil and gas price returns as a state variable, provides strong support for oil prices being an important explanatory variable for stock returns. [Copyright &y& Elsevier]

This paper presents the impact of numerical methods used to solve the problem of synthesis of signals modulated in frequency with an autocorrelation function that implements an optimal approximation to a given autocorrelation function. After theoretical introduction an algorithm of frequency modulated signal synthesis is presented. Simulation results made in Matlab are presented in the last section. [ABSTRACT FROM AUTHOR]

Abstract: We propose a novel homogeneous neural network ensemble approach called Generalized Regression Neural Network (GEFTS–GRNN) Ensemble for Forecasting Time Series, which is a concatenation of existing machine learning algorithms. GEFTS uses a dynamic nonlinear weighting system wherein the outputs from several base-level GRNNs are combined using a combiner GRNN to produce the final output. We compare GEFTS with the 11 most used algorithms on 30 real datasets. The proposed algorithm appears to be more powerful than existing ones. Unlike conventional algorithms, GEFTS is effective in forecasting time series with seasonal patterns. [Copyright &y& Elsevier]

As the use of spreadsheet packages for statistical analysis increases, so does the need for assessing the reliability of these packages. This study compares the accuracy of six spreadsheet packages: Excel, Google Docs, Gnumeric, Numbers, OpenOffice Calc, and Quattro Pro. The National Institute of Standards and Technology (NIST) compiled sets of data specifically to test for computational accuracy. Certified statistically accurate computations for standard statistical procedures accompany these datasets. This study analyzes the accuracy of summary statistics such as the mean, standard deviation, and autocorrelation as well as the F statistics for a one-way ANOVA, and the coefficients and R2statistics in regression analysis using the Statistical Reference Datasets (StRD) provided by NIST. Wilkinson's Tests are also examined to document a package's ability to perform rounding, univariate statistics, scatterplots, and regression/correlation with particularly challenging data. The final analysis reports the accuracy of probability and percentile computations involving statistical distributions. The results suggest that Gnumeric is the most reliable both in performing statistical analysis and for calculations involving statistical distributions. Google Docs spreadsheet, while convenient, has deficiencies and should not be used for scientific statistical analysis. This article has supplementary material online. [ABSTRACT FROM AUTHOR]

Abstract: The association between the monthly total ozone concentration and monthly maximum temperature over Kolkata (22.56°N, 88.30° E), India, has been explored in this paper. For this, the predictability of monthly maximum temperature based on the total ozone as predictor is investigated using Artificial Neural Network. The presence of persistence and similar cyclic patterns are revealed through autocorrelation and cross-correlation coefficients. Common cycles of length 12 and 6 have been identified through periodogram. Hence, a predictive model has been generated by Artificial Neural Network in the form of Multi Layer Perceptron (MLP) using scaled conjugate gradient learning with sigmoid non-linearity. After training and testing the network, an MLP with total ozone of month n as predictor and maximum temperature of month (n +1) as the target output is found as the best model. Performance of the model has been judged statistically. Finally, the MLP model has been compared with linear and non-linear regressions and the efficiency of MLP has been established over the regression models. [Copyright &y& Elsevier]

Abstract: Artificial neural networks (ANNs) and Bayesian belief networks (BBNs) utilizing select environmental variables were developed and evaluated, with the intent to model net ecosystem metabolism (a proxy for system trophic state) within a freshwater wetland. Network modeling was completed independently for distinct data subsets, representing periods of ‘low’ and ‘high’ water levels throughout in the wetland. ANNs and BBNs were ‘benchmarked’ against traditional parametric analyses, with network architectures outperforming regression models. ANNs delivered the greatest predictive accuracy for NEM and did not require expert knowledge about system variables for their development. BBNs provided users with an interactive diagram depicting predictor interaction and the qualitative/quantitative effects of variable dynamics upon NEM, thereby affording better information extraction. Importantly, BBNs accommodated the imbalanced nature of the dataset and appeared less affected (than ANNs) with variable auto-correlation traits that are typically observed within large and ‘noisy’ environmental datasets. [Copyright &y& Elsevier]

Abstract: Intensification of agriculture and industry in salinized areas poses a risk of secondary salinization. Thus, comprehensive and spatially explicit assessments are needed to assist government in developing ecologically sound policies. Few assessments have comprehensively quantified the impacts of multiple anthropogenic activities on salinization as environmental interferences and salinity autocorrelation are largely neglected. This study tried to perform such an assessment by identifying the nature of human impacts on salinization from three aspects in the Yellow River Delta (YRD) of China. A versatile GIS-based spatial autoregression (SAR) was applied to nine selected explainable variables in six sub-region models. Sub-region model was verified as an effective tool of normalizing environmental interferences because more useful spatial information was provided compared to the whole region model. GIS-SAR model fit better and performed better in quantifying human activities, compared to the conventional ordinary least square regression (OLSR) model, as SAR can deal with spatial autocorrelation in soil salinity. Among the well-defined key determinants, oil exploitation and saline aquaculture were aggregative to salinization but only in originally highly saline sub-regions, such as coastal zone and Gleyic Solonchaks (coastal saline moisture soil) area. Two agricultural activities, crop plantation and fertilization, were mainly ameliorators in most sub-regions. The most effective salinization alleviation occurred in moderately saline sub-regions, such as floodplain and Salic Fluvisols (saline moisture soil) area, which benefitted from the development of agroforests and farm ponds. The SAR sub-region model is spatially explicit for spotting the hazardous areas and some suggestions were also provided for the policy makers. [Copyright &y& Elsevier]

The problem of testing non-nested regression models that include lagged values of the dependent variable as regressors is discussed. It is argued that it is essential to test for error autocorrelation if ordinary least squares and the associated J and F tests are to be used. A heteroskedasticity-robust joint test against a combination of the artificial alternatives used for autocorrelation and non-nested hypothesis tests is proposed. Monte Carlo results indicate that implementing this joint test using a wild bootstrap method leads to a well-behaved procedure and gives better control of finite sample significance levels than asymptotic critical values. [ABSTRACT FROM AUTHOR]

Abstract: We analyze the statistical properties of three price discovery measures: The variance ratio, the weighted price contribution (WPC), and the R 2 of unbiasedness regressions. We find that, if the price process is a driftless martingale, only the WPC is an unbiased estimator for the return variance explained during a time interval. For autocorrelated processes with a drift, only the R 2 of the unbiasedness regression is consistent, but it is biased for small samples. [Copyright &y& Elsevier]

Studies of performance persistence of closed-end funds (CEFs) use two measures of persistence; autocorrelation and rank correlation of performance. The autocorrelation measure offers limited information because it cannot separate persistence relative to the market and to the industry. The rank correlation measure is generally applied to two periods, disregarding multi-period persistence. We investigate performance persistence of CEFs in terms of both market price return and net asset value return using contingency tables and multiple regression models. Jensen's alpha and the Sharpe ratio are used as measures of risk-adjusted performance. We test three hypotheses: (i) CEFs performing better than the industry median will do so persistently, (ii) CEFs outperform the market persistently; and (iii) performance persistence can be partly explained by dividend yield. The findings are fivefold. First, the number of persistent years varies with the models used to calculate risk-adjusted performance. Second, with 4-index unconditional beta fixed variance model, CEFs persistently beat their industry for six out of 10 years in terms of both market price return and net asset value return. Third, with a 4-index unconditional beta fixed variance model, we find performance persistence relative to market for 6 and 7 years, out of the 10 years considered, in terms of market price return and net asset value return, respectively. Fourth, the disaggregate sample tests show that performance of municipal bond funds is more persistent than equity funds and taxable bond funds. Fifth, dividend patterns can partially explain persistence with liquidity as control. [ABSTRACT FROM AUTHOR]

Purpose: To develop the quantitative structure-activity relationship (QSAR) for predicting the anticonvulsant activity of _-substituted acetamido-N-benzylacetamide derivatives. Methods: AM1 semiempirical quantum chemical calculation method was used to find the optimum 3D geometry of the studied molecules. Two types of molecular descriptors, including the 2D autocorrelation and GETAWAY descriptors, were used to derive a quantitative relation between anticonvulsant activity and structural properties. The relevant molecular descriptors were selected by genetic algorithm-based multiple linear regression (GA-MLR) approach. Results: The high value of the correlation coefficient, R2 (0.900), indicate that the model was satisfactory. Conclusion: The proposed model has good stability, robustness and predictability when verified by internal and external validation. [ABSTRACT FROM AUTHOR]

The network autocorrelation model has been a workhorse for modeling network influences on individual behavior. The standard network approaches to mapping social influence using network measures, however, are limited to specifying an influence weight matrix (W) based on a single mode network. Additionally, it has been demonstrated that the estimate of the autocorrelation parameter ρ of the network effect tends to be negatively biased as the density in W matrix increases. The current study introduces a two-mode version of the network autocorrelation model. We then conduct simulations to examine conditions under which bias might exist. We show that the estimate for the affiliation autocorrelation parameter (ρ) tends to be negatively biased as density increases, as in the one-mode case. Inclusion of the diagonal of W, the count of the number of events participated in, as one of the variables in the regression model helps to attenuate such bias, however. We discuss the implications of these results. [Copyright &y& Elsevier]

We propose simple and robust diagnostic tests for spatial error autocorrelation and spatial lag dependence. The idea is to reformulate the testing problem such that the outer product of gradients (OPG) variant of the LM test can be employed. Our versions of the tests are based on simple auxiliary regressions, where ordinary regression t- and F-statistics can be used to test for spatial autocorrelation and lag dependence. An important advantage of the proposed test statistics is that they are robust against heteroscedastic errors. Therefore, our approach gives practitioners an easy to implement and robust alternative to existing tests. [ABSTRACT FROM AUTHOR]

The histamine H4 receptor binding affinity of 2-(4-methylpiperazin-1-yl)quinoxaline derivatives has been quantitatively analyzed in terms of Dragon descriptors. The derived QSAR models have provided rationales to explain the activity of titled derivatives. The descriptors identified in CP-MLR analysis have highlighted the role of path/walk 4-Randic shape index (PW4), mean square distance (MSD) index, topological charges (GGI9, JGI2, and JGI7), atomic properties in respective lags of 2D-autocorrelations (MATS7e, GATS7e, and MATS8p), and Burden matrix (BELm1) to explain the binding affinity. Certain structural fragments (C-002 and C-027) have also shown prevalence to optimize the H4R binding affinity of titled compounds. The PLS analysis has also confirmed the dominance of information content of CP-MLR-identified descriptors for modelling the activity. [ABSTRACT FROM AUTHOR]

We study the finite-sample performance of test statistics in linear regression models where the error dependence is of unknown form. With an unknown dependence structure, there is traditionally a trade-off between the maximum lag over which the correlation is estimated (the bandwidth) and the amount of heterogeneity in the process. When allowing for heterogeneity, through conditional heteroskedasticity, the correlation at far lags is generally omitted and the resultant inflation of the empirical size of test statistics has long been recognized. To allow for correlation at far lags, we study the test statistics constructed under the possibly misspecified assumption of conditional homoskedasticity. To improve the accuracy of the test statistics, we employ the second-order asymptotic refinement in Rothenberg [Approximate power functions for some robust tests of regression coefficients, Econometrica 56 (1988), pp. 997-1019] to determine the critical values. The simulation results of this paper suggest that when sample sizes are small, modelling the heterogeneity of a process is secondary to accounting for dependence. We find that a conditionally homoskedastic covariance matrix estimator (when used in conjunction with Rothenberg's second-order critical value adjustment) improves test size with only a minimal loss in test power, even when the data manifest significant amounts of heteroskedasticity. In some specifications, the size inflation was cut by nearly 40% over the traditional heteroskedasticity and autocorrelation consistent (HAC) test. Finally, we note that the proposed test statistics do not require that the researcher specify the bandwidth or the kernel. [ABSTRACT FROM AUTHOR]

Abstract: A new method for deriving effect sizes from single-case designs is proposed. The strategy is applicable to small-sample time-series data with autoregressive errors. The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce an effect size that represents the magnitude of treatment effect from baseline to treatment phases in standard deviation units. In this paper, the method is applied to two published examples using common single case designs (i.e., withdrawal and multiple-baseline). The results from these studies are described, and the method is compared to ten desirable criteria for single-case effect sizes. Based on the results of this application, we conclude with observations about the use of GLS as a support to visual analysis, provide recommendations for future research, and describe implications for practice. [Copyright &y& Elsevier]

A two-step Lagrange Multiplier test strategy has recently been suggested as a device to reveal spatial nonstationarity and spurious spatial regression. The present paper generalises this procedure by incorporating control for unobserved heteroscedasticity. Using Monte Carlo simulation, the behaviour of several relevant tests for nonstationarity and/or heteroscedasticity is investigated. The two-step Lagrange Multiplier test for spatial nonstationarity turns out to be robust towards heteroscedasticity. While several tests for heteroscedasticity prove inconclusive under certain circumstances, it is shown that a Lagrange Multiplier test for heteroscedasticity based on spatially differenced variables serves well as an indication of heteroscedasticity irrespective of stationarity status. [ABSTRACT FROM AUTHOR]

We present an improved method for inference in linear regressions with overlapping observations. By aggregating the matrix of explanatory variables in a simple way, our method transforms the original regression into an equivalent representation in which the dependent variables are non-overlapping. This transformation removes that part of the autocorrelation in the error terms which is induced by the overlapping scheme. Our method can easily be applied within standard software packages since conventional inference procedures (OLS-, White-, Newey-West- standard errors) are asymptotically valid when applied to the transformed regression. Through Monte Carlo analysis we show that it performs better in finite samples than the methods applied to the original regression that are in common usage. We illustrate the significance of our method with three empirical applications. [ABSTRACT FROM AUTHOR]

Species distribution models are an important tool to predict the impact of global change on species distributional ranges and community assemblages. Although considerable progress has been made in the statistical modeling during the last decade, many approaches still ignore important features of species distributions, such as nonlinearity and interactions between predictors, spatial autocorrelation, and nonstationarity, or at most incorporate only some of these features. Ecologists, however, require a modeling framework that simultaneously addresses all these features flexibly and consistently. Here we describe such an approach that allows the estimation of the global effects of environmental variables in addition to local components dealing with spatiotemporal autocorrelation as well as nonstationary effects. The local components can be used to infer unknown spatiotemporal processes; the global component describes how the species is influenced by the environment and can be used for predictions, allowing the fitting of many well-known regression relationships, ranging from simple linear models to complex decision trees or from additive models to models inspired by machine learning procedures. The reliability of spatiotemporal predictions can be qualitatively predicted by separately evaluating the importance of local and global effects. We demonstrate the potential of the new approach by modeling the breeding distribution of the Red Kite (Milvus milvus), a bird of prey occurring predominantly in Western Europe, based on presence/absence data from two mapping campaigns using grids of 40 km2 in Bavaria. The global component of the model selected seven environmental variables extracted from the CORINE and WorldClim databases to predict Red Kite breeding. The effect of altitude was found to be nonstationary in space, and in addition, the data were spatially autocorrelated, which suggests that a species distribution model that does not allow for spatially varying effects and spatial autocorrelation would have ignored important processes determining the distribution of Red Kite breeding across Bavaria. Thus, predictions from standard species distribution models that do not allow for real-world complexities may be considerably erroneous. Our analysis of Red Kite breeding exemplifies the potential of the innovative approach for species - distribution models. The method is also applicable to modeling count data. [ABSTRACT FROM AUTHOR]

This paper deals with the linear regression model with measurement errors in both response and covariates. The variables are observed with errors together with an auxiliary variable, such as time, and the errors in response are autocorrelated. We propose a weighted denoised minimum distance estimator (WDMDE) for the regression coefficients. The consistency, asymptotic normality, and strong convergence rate of the WDMDE are proved. Compared with the usual denoised least squares estimator (DLSE) in the previous literature, the WDMDE is asymptotically more efficient in the sense of having smaller variances. It also avoids undersmoothing the regressor functions over the auxiliary variable, so that data-driven optimal choice of the bandwidth can be used. Furthermore, we consider the fitting of the error structure, construct the estimators of the autocorrelation coefficients and the error variances, and derive their large-sample properties. Simulations are conducted to examine the finite sample performance of the proposed estimators, and an application of our methodology to analyze a set of real data is illustrated as well. [ABSTRACT FROM AUTHOR]

The paper re-examines the issue of the robustness of the weekday effect. Specifically, by utilizing a quantile regression approach, the homogeneity of observed day-of-the-week anomalies is monitored and tested over different parts of the conditional return distribution. The day-of-the-week effects are measured for conditional returns as well as for conditional volatilities. The model applied accounts for asymmetry in first-order autocorrelation in both moments. The weekday patterns in the returns on the European market index and 18 European country indexes are analyzed for the time period from January 2000 through December 2006. Generally, the sign of the estimated weekday effects in both the conditional mean and volatility seems to be very robust over the return distribution. However, about one half of the country-specific indexes exhibit significant variation or asymmetry in the day-of-the-week coefficients across the quantiles of the conditional return distribution. Only in a few cases, the significant day-of-the-week effect is clearly driven by extreme events. [ABSTRACT FROM AUTHOR]

Indirect models provide an intuitive and effective means for incorporating knowledge on structured class definitions or functional relationships into classification and regression models for complex data. We transfer the approach of generalized indirect classification to the regression context, and show in a case study that the predictive performance of direct regression methods can be significantly improved by incorporating classification models for intermediate categorical variables to construct indirect regression models. We apply this approach to model winter road maintenance on Ontario highways as a function of hourly time series of meteorological data. Two ordinal variables representing different salt application modes are used as intermediate variables. Stepwise linear regression as a direct regression technique is outperformed by an indirect linear regression using bagging and penalized linear discriminant analysis (PLDA) as intermediate models. In addition, direct bagging is outperformed by indirect bagging with classification trees and PLDA as intermediate models. The latter indirect method achieves the best performance of all four approaches examined, reducing the median bootstrapped RMSE significantly by 6.9-10.4% compared to the other three methods. The bootstrap is performed at the day level to account for temporal autocorrelation. In addition, permutation-based variable accuracy importance measures are applied to assess the utility of different variables and data sources. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper develops a method to capture anisotropic spatial autocorrelation in the context of the simultaneous autoregressive model. Standard isotropic models assume that spatial correlation is a homogeneous function of distance. This assumption, however, is oversimplified if spatial dependence changes with direction. We thus propose a local anisotropic approach based on non-linear scale-space image processing. We illustrate the methodology by using data on single-family house transactions in Lucas County, Ohio. The empirical results suggest that the anisotropic modeling technique can reduce both in-sample and out-of-sample forecast errors. Moreover, it can easily be applied to other spatial econometric functional and kernel forms. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of equity markets and top incomes on art prices. Using a newly constructed art market index, we demonstrate that equity market returns have had a significant impact on the price level in the art market over the last two centuries. We also find evidence that an increase in income inequality may lead to higher prices for art. Finally, the results of Johansen's cointegration tests strongly suggest the existence of a long-run relation between top incomes and art prices. [ABSTRACT FROM AUTHOR]

The computation of Moran's index of spatial autocorrelation requires the definition of a spatial weighting matrix. The eigendecomposition of this doubly centered matrix (i.e., one that forces the sums of all rows and columns to equal zero) has interesting properties that have been exploited in various contexts: distribution properties of the Moran coefficient (MC), spatial filtering in linear models, generalized linear models, and multivariate analysis. In this article, this eigendecomposition is used to propose a new view of MC based on its interpretation in the simple context of linear regression. I use this interpretation to demonstrate the different properties of MC and also the inefficiency of this index in some situations involving simultaneous positive and negative spatial autocorrelation. I propose some new statistics and procedures for testing spatial autocorrelation, and conduct a simulation study to evaluate these new approaches. [ABSTRACT FROM AUTHOR]

In this article, the linearization-based approach was extended in order to work for the functions defined by the orthogonal terms and not only the truth tables or the decision diagrams. The article proposes a method for calculation and compact representation of the autocorrelation function for the functions defined by a set of orthogonal terms.

Abstract: This paper studies the relationship between homicide rate and socioeconomic factors at community area level in Chicago from 1960 to 1995. Most of prior studies of social disorganization theory are based on cross-sectional spatial regression or longitudinal studies. This research integrates space and time in testing social disorganization theory. First, exploratory spatial data analysis (ESDA) is used to examine dynamic spatial patterns of these indicators. This investigation justifies the estimation of homicide rates across community areas through panel-data models that extend to include spatial lag and spatial error autocorrelation. [Copyright &y& Elsevier]

This study examines whether or not there is an optimal firm size for publicly traded US hotels. More specifically, the study tests a threestage relationship based on economies and diseconomies of scale: the first stage, in which firm value increases as firm size increases; the second stage, in which firm value remains constant as firm size increases beyond the first stage; and the third stage, in which firm value decreases as firm size transcends the second stage. The study uses the Newey–West heteroskedasticity and the autocorrelation consistent (HAC) standard errors estimation in pooled regression analysis. Findings partially support the proposed relationship. [ABSTRACT FROM AUTHOR]

This paper investigates the application of linear regression models and modeling techniques in predicting freight generation at the national level within the U.S. Specifically, the paper seeks to improve the performance and fit of linear regression models of freight generation. We provide insight into different variable transformation techniques, evaluate the use of spatial regression variables, and apply a spatial regression modeling methodology to correct for spatial autocorrelation. We conclude that the spatial regression model is the preferred specification for freight generation at the national level. The proliferation of Geographic Information Systems (GIS) within planning agencies affords more widespread use of spatial regression and our results indicate this technique would provide improvement to models that have been traditionally limited by insufficient data. [ABSTRACT FROM AUTHOR]

Abstract: This paper attempts to explain the spatial variation of the use of a bicycle for commuting to work at the level of the 589 municipalities in Belgium. Regression techniques were used and special attention was paid to autocorrelation, heterogeneity and multicollinearity. Spatial lag models were used to correct for the presence of spatial dependence and a disaggregated modelling strategy was adopted for the northern and southern parts of the country. The results show that much of the inter-municipality variation in bicycle use is related to environmental aspects such as the relief, traffic volumes and cycling accidents. Town size, distance travelled and demographic aspects also have some effect. In addition, there are regional differences in the effects of the structural covariates on bicycle use: the impact of variables such as traffic volume and cycling accidents differs substantially between the north and the south of the country. This paper also suggests that high rates of bicycle use in one municipality stimulate cycling in neighbouring municipalities, and hence that a mass effect can be initiated, i.e. more cycle commuting encourages even more commuters in the area to cycle. These findings provide some recommendations for decision-makers wishing to promote a shift from car to bicycle use. [ABSTRACT FROM AUTHOR]

In March 2008 the New South Wales judiciary restricted pub closing times to 3 a.m., and later 3.30 a.m., in the central business district (CBD) of Newcastle, Australia. We sought to determine whether the restriction reduced the incidence of assault. Non-equivalent control group design with before and after observations. Newcastle, a city of 530 000 people. People apprehended for assault in the CBD and nearby Hamilton, an area with a similar night-time economy but where no restriction was imposed. Police-recorded assaults in the CBD before and after the restriction were compared with those in Hamilton. Cases were assaults occurring from 10 p.m.-6 a.m. from January 2001-March 2008, with April 2008-September 2009 as the post-restriction period. We also examined changes in assault incidence by time of night. Negative binomial regression with time, area, time × area interaction terms and terms for secular trend and seasonal effects was used to analyse the data. Autocorrelation was examined using generalized estimating equations. In the CBD, recorded assaults fell from 99.0 per quarter before the restriction to 67.7 per quarter afterward [incidence rate ratio (IRR): 0.66, 95% confidence interval (CI): 0.55-0.80]. In the same periods in Hamilton, assault rates were 23.4 and 25.5 per quarter, respectively (IRR: 1.02, 95% CI: 0.79-1.31). The relative reduction attributable to the intervention was 37% (IRR = 0.63, 95% CI: 0.47-0.81) and approximately 33 assault incidents were prevented per quarter. This study indicates that a restriction in pub closing times to 3/3.30 a.m. in Newcastle, NSW, produced a large relative reduction in assault incidence of 37% in comparison to a control locality. [ABSTRACT FROM AUTHOR]

Two centuries ago large areas of north-west Europe were covered by coherent heathlands which hosted numerous specialized species. Changes in land use made heathlands fragmented and rare, consequently, they are in the focus of nature conservation efforts today. But how large should remaining heathland patches be in order to secure the survival of populations of specialized species? We investigated the genetic diversity at five allozyme loci of Poecilus lepidus, a flightless and stenotopic heathland ground beetle. 29 populations from differently sized heathland patches in north-west Germany were analyzed. Results show a weak but significant genetic differentiation and no evidence for isolation by distance or other patterns of spatial autocorrelation. Linear regression analysis revealed significant relationships between patch size, allelic richness, number of alleles and expected heterozygosity. These findings are explained by severe habitat fragmentation together with strong fluctuations in population size which have been reported for this species in the past. To conserve the vast majority of the species' genetic diversity for a period of 100 years we suggest to maintain heathland patches of at least 50 ha in size. [ABSTRACT FROM AUTHOR]

Background: Comparing metabolic profiles under different biological perturbations has become a powerful approach to investigating the functioning of cells. The profiles can be taken as single snapshots of a system, but more information is gained if they are measured longitudinally over time. The results are short time series consisting of relatively sparse data that cannot be analyzed effectively with standard time series techniques, such as autocorrelation and frequency domain methods. In this work, we study longitudinal time series profiles of glucose consumption in the yeast Saccharomyces cerevisiae under different temperatures and preconditioning regimens, which we obtained with methods of in vivo nuclear magnetic resonance (NMR) spectroscopy. For the statistical analysis we first fit several nonlinear mixed effect regression models to the longitudinal profiles and then used an ANOVA likelihood ratio method in order to test for significant differences between the profiles. Results: The proposed methods are capable of distinguishing metabolic time trends resulting from different treatments and associate significance levels to these differences. Among several nonlinear mixed-effects regression models tested, a three-parameter logistic function represents the data with highest accuracy. ANOVA and likelihood ratio tests suggest that there are significant differences between the glucose consumption rate profiles for cells that had been-or had not been-preconditioned by heat during growth. Furthermore, pair-wise t-tests reveal significant differences in the longitudinal profiles for glucose consumption rates between optimal conditions and heat stress, optimal and recovery conditions, and heat stress and recovery conditions (p-values <0.0001). Conclusion: We have developed a nonlinear mixed effects model that is appropriate for the analysis of sparse metabolic and physiological time profiles. The model permits sound statistical inference procedures, based on ANOVA likelihood ratio tests, for testing the significance of differences between short time course data under different biological perturbations. [ABSTRACT FROM AUTHOR]

A quantitative structure-anti HIV-1 activity relationship study has been applied in a series of 1-[2-hydroxyethoxy-methyl]-6-(phenylthio)thymine] analogues acting as non-nucleoside reverse transcriptase inhibitors. The relevant 2D autocorrelation descriptors for deriving a quantitative relation between the anti-HIV activity and structural properties were selected by the multiple linear regression approach. Analysis of the resulting model revealed a correlation coefficient and a root mean square error of 0.859 and 0.503, respectively. The predictive ability of the model indicates that this model can be used for virtual library screening of databases for novel potent anti-HIV agents. [ABSTRACT FROM AUTHOR]

This article discusses the modelling of integer-valued time series with overdispersion and potential extreme observations. For the problem, a negative binomial INGARCH model, a generalization of the Poisson INGARCH model, is proposed and stationarity conditions are given as well as the autocorrelation function. For estimation, we present three approaches with the focus on the maximum likelihood approach. Some results from numerical studies are presented and indicate that the proposed methodology performs better than the Poisson and double Poisson model-based methods. [ABSTRACT FROM AUTHOR]

We present a new linear regression model for use with aggregated, small area data that are spatially autocorrelated. Because these data are aggregates of individual-level data, we choose to model the spatial autocorrelation using a geostatistical model specified at the scale of the individual. The autocovariance of observed small area data is determined via the natural aggregation over the population. Unlike lattice-based autoregressive approaches, the geostatistical approach is invariant to the scale of data aggregation. We establish that this geostatistical approach also is a valid autoregressive model; thus, we call this approach the geostatistical autoregressive (GAR) model. An asymptotically consistent and efficient maximum likelihood estimator is derived for the GAR model. Finite sample evidence from simulation experiments demonstrates the relative efficiency properties of the GAR model. Furthermore, while aggregation results in less efficient estimates than disaggregated data, the GAR model provides the most efficient estimates from the data that are available. These results suggest that the GAR model should be considered as part of a spatial analyst's toolbox when aggregated, small area data are analyzed. More important, we believe that the GAR model's attention to the individual-level scale allows for a more flexible and theory-informed specification than the existing autoregressive approaches based on an area-level spatial weights matrix. Because many spatial process models, both in geography and in other disciplines, are specified at the individual level, we hope that the GAR covariance specification will provide a vehicle for a better informed and more interdisciplinary use of spatial regression models with area-aggregated data. [ABSTRACT FROM AUTHOR]

Efficient score tests exist among others, for testing the presence of additive and/or innovative outliers that are the result of the shifted mean of the error process under the regression model. A sample influence function of autocorrelation-based diagnostic technique also exists for the detection of outliers that are the result of the shifted autocorrelations. The later diagnostic technique is however not useful if the outlying observation does not affect the autocorrelation structure but is generated due to an inflation in the variance of the error process under the regression model. In this paper, we develop a unified maximum studentized type test which is applicable for testing the additive and innovative outliers as well as variance shifted outliers that may or may not affect the autocorrelation structure of the outlier free time series observations. Since the computation of the p-values for the maximum studentized type test is not easy in general, we propose a Satterthwaite type approximation based on suitable doubly non-central F-distributions for finding such p-values [F.E. Satterthwaite, An approximate distribution of estimates of variance components, Biometrics 2 (1946), pp. 110-114]. The approximations are evaluated through a simulation study, for example, for the detection of additive and innovative outliers as well as variance shifted outliers that do not affect the autocorrelation structure of the outlier free time series observations. Some simulation results on model misspecification effects on outlier detection are also provided. [ABSTRACT FROM AUTHOR]

In the present study, a seasonal and non-seasonal prediction of the Standardized Precipitation Index (SPI) time series is addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to predict drought in the Büyük Menderes river basin using SPI as drought index. Temporal characteristics of droughts based on SPI as an indicator of drought severity indicate that the basin is affected by severe and more or less prolonged periods of drought from 1975 to 2006. Therefore, drought prediction plays an important role for water resources management. ARIMA modeling approach involves the following three steps: model identification, parameter estimation, diagnostic checking. In model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results of the SPI series, different ARIMA models are identified. The model gives the minimum Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) is selected as the best fit model. Parameter estimation step indicates that the estimated model parameters are significantly different from zero. Diagnostic check step is applied to the residuals of the selected ARIMA models and the results indicated that the residuals are independent, normally distributed and homoscedastic. For the model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted data show reasonably good agreement with the actual data. The ARIMA models developed to predict drought found to give acceptable results up to 2 months ahead. The stochastic models developed for the Büyük Menderes river basin can be employed to predict droughts up to 2 months of lead time with reasonably accuracy. [ABSTRACT FROM AUTHOR]

Abstract: In the Lower Yellow River Delta, soil salinity is a problem due to the presence of a shallow, saline water table and marine sediments. Spatial information on soil salinity at the field level is increasingly needed, particularly for better soil management and crop allocation in this area. In this paper, a mobile electromagnetic induction (EMI) system including EM38 and EM31 is employed to perform field electromagnetic (EM) survey, and fast determination and quantitative evaluation of the spatial pattern of soil salinity is discussed using the field EM survey data. Optimal operation modes of EM38 and EM31 are determined to establish multiple linear regression models for estimating salinity from apparent soil electrical conductivity (ECa). Spatial trend and semivariogram are illustrated and spatial distribution of field salinity status is further visualized and quantitatified. The results suggest that ECa (EM38 and EM31) data is highly correlated with salinity, and that the interpretation precision of soil salinity at various layers can be improved using EM38h and EM31h (where h represents the horizontal mode of EM measurement). Both EM38h and EM31h exhibit significant geographic trend. Nested spherical models fit the semivariance of EM38h and EM31h better than single spherical models. Spatial autocorrelation of EM31h is stronger than that of EM38h, and short-range variation is the chief constitute of spatial heterogeneity for both EM38h and EM31h. Quantitative classification shows that soil salinity exhibits the trend of accumulation in the root zone. In 0–1.0m solum, heavy salinized and saline soils are the predominant soil types, accounting for 54% and 41% of total survey area, respectively. The area of light and moderate salinized soils is comparatively small, which accounts for only 0.4% and 4.6%, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a time series analysis of historical observations of wind speed in order to project future wind speed trends. For this study, 52 years of data have been used from seven suitable stations across the UK. Four parsimonious models have been employed, and the data were split into two different segments: the training and the validation data sets. During the fitting process, the optimum parameters for each model were determined in order to minimize the mean square error in the predictions. The results suggest that the seasonal pattern in wind speeds is the most important factor but that there is some monthly autocorrelation in the data, which can improve forecasts. This is confirmed by testing the four models with the model having considered both autocorrelation and seasonality achieving the smallest errors. The approach proposed for forecasting wind speeds a month ahead may be deemed useful to suppliers for purchasing base load in advance and to system operators for power system maintenance scheduling up to a month ahead. [ABSTRACT FROM AUTHOR]

Accurate characterization of line-edge roughness (LER) and line-width roughness (LWR) is essential to cope with the growing challenge of device variability in large-scale integrations. The accuracy is affected markedly by statistical noise, which is caused by the finiteness of a number of samples. The statistical noise produces random oscillatory fluctuations of autocorrelation function (ACF) of LER/LWR. These fluctuations are obstacles to estimating LWR statistics by comparing experimental and theoretical ACFs. Using the Monte Carlo (MC) method to prepare pseudoexperimental ACFs (MC-ACFs), the authors found that an error η of the estimates is minimized in the case when a ratio of a fitting-window size to a correlation length is 0.3 or smaller, being less affected by the statistical noise. η under a fixed sampling interval is determined by the total number Nall of width data used to obtain the MC-ACF. This comes from the fact that the MC-ACF is obtained after averaging approximately for Nall times. The authors also investigated the case when LWR consisted of two components that had different correlation lengths. They confirmed that η of both components increase with a decrease in their occupancies in the entire LWR. This, together with a large correlation length, makes it difficult to accurately characterize the longer-correlation component, which is mostly minor (small occupancy) in actual cases. This difficulty is also an obstacle to estimating the shorter-correlation component, because the statistics of the former are mostly the prerequisites for analyzing the latter. These facts make a stark contrast to a power-spectral-density (PSD) fitting method, where at least the shorter-correlation component is estimated with almost the same accuracy as in the case of a single component. Based on these results, the authors propose to investigate PSDs, rather than ACFs, in the case of multicomponent LWR. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their NMSEs are 0.02314 and 0.15384 respectively. [ABSTRACT FROM AUTHOR]

In this paper method available for generating ternary sequences is discussed. These sequences are useful in many applications but specifically in synchronization of block codes and pulse compression in radar. The ternary sequences are derived from chaotic maps. It is feasible to achieve simultaneously superior performances in detection range and range resolution using the proposed ternary sequences. The properties of these sequences like autocorrelation function, Peak Side Lobe Ratio (PSLR), ambiguity diagram and performance under AWGN noise background has been studied. The generation of these sequences is much simpler, and the available number of sequences is virtually infinite and not limited by the length of the sequence. [ABSTRACT FROM AUTHOR]