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Taxi fare is related to the daily life of residents. Reasonable taxi fare not only meets the travel demand of residents but also improves the income of drivers and promotes employment. The spatial variation of taxi fares exists between different regions and cities. Previous studies on taxi fares have been conducted mostly in individual cities, and there has been no study on the spatial differentiation pattern of taxi fares on a national scale. Taking 336 cities across China as the research object, a multiple linear regression model of taxi fares was established by demonstrating the spatial variation pattern of taxi fares, the global differentiation index, spatial autocorrelation analysis, and kernel density analysis, etc. The significance of the study is to explore the law of spatial differentiation of taxi fares in China and to provide a stenographic record of taxi fare adjustment. The results show that: (1) the spatial variation of taxi fares across the country is significant, with the starting taxi fare range being between RMB 4 and RMB 14. (2) The global differentiation index of taxi fares is large, with two low-low clusters and three high-high clusters appearing spatially, and the results of the kernel density analysis surface a dispersion distribution centered on provincial capitals. (3) The divergence pattern of taxis nationwide is influenced by several factors. A multiple linear regression model is selected to establish a multiple linear regression model of urban disposable income per capita, regional GDP, urbanization rate, and urban population density, which shows that urban disposable income per capita has the greatest influence on taxi fares. The model shows that urban disposable income per capita has the greatest influence on taxi fares, and the fare of a 5 km taxi ride is 6.07. Taxi fares have a clear pattern of spatial differentiation in China and are most affected by urban disposable income per capita. Through this study, we can gain a deeper understanding of the variation in taxi fares across the country and provide data and theoretical support for the rationality of taxi fare adjustments. [ABSTRACT FROM AUTHOR]

Linear regression models are commonly used for estimating ground PM2.5 concentrations, but the global spatial autocorrelation and local spatial heterogeneity of PM2.5 distribution are either ignored or only partially considered in commonly used models for estimating PM2.5 concentrations. Therefore, taking both global spatial autocorrelation and local spatial heterogeneity into consideration, a global-local regression (GLR) model is proposed for estimating ground PM2.5 concentrations in the Yangtze River Delta (YRD) and in the Beijing, Tianjin, Hebei (BTH) regions of China based on the aerosol optical depth data, meteorological data, remote sensing data, and pollution source data. Considering the global spatial autocorrelation, the GLR model extracts global factors by the eigenvector spatial filtering (ESF) method, and combines the fraction of them that passes further filtering with the geographically weighted regression (GWR) method to address the local spatial heterogeneity. Comprehensive results show that the GLR model outperforms the ordinary GWR and ESF models, and the GLR model has the best performance at the monthly, seasonal, and annual levels. The average adjusted R2 of the monthly GLR model in the YRD region (the BTH region) is 0.620 (0.853), which is 8.0% and 7.4% (6.8% and 7.0%) higher than that of the monthly ESF and GWR models, respectively. The average cross-validation root mean square error of the monthly GLR model is 7.024 μg/m3 in the YRD region, and 9.499 μg/m3 in the BTH region, which is lower than that of the ESF and GWR models. The GLR model can effectively address the spatial autocorrelation and spatial heterogeneity, and overcome the shortcoming of the ordinary GWR model that overfocuses on local features and the disadvantage of the poor local performance of the ordinary ESF model. Overall, the GLR model with good spatial and temporal applicability is a promising method for estimating PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

Increasingly, deformation prediction has become an essential research topic in sluice safety control, which requires significant attention. However, there is still a lack of practical and efficient prediction modeling for sluice deformation. In order to address the limitations in mining the deep features of long-time data series of the traditional statistical model, in this paper, an improved long short-term memory (LSTM) model and weighted Markov model are introduced to predict sluice deformation. In the method, the seagull optimization algorithm (SOA) is utilized to optimize the hyper-parameters of the neural network structure in LSTM primarily to improve the model. Subsequently, the relevant error sequences of the fitting results of SOA-LSTM model are classified and the Markovity of the state sequence is examined. Then, the autocorrelation coefficients and weights of each order are calculated and the weighted and maximum probability values are applied to predict the future random state of the sluice deformation. Afterwards, the prediction model of sluice deformation on the SOA-LSTM-weighted Markov model is proposed. Ultimately, the presented model is used to predict the settlement characteristics of an actual sluice project in China. The analysis results demonstrate that the proposed model possesses the highest values of R2 and the smallest values of RMSE and absolute relative errors for the monitoring data of four monitoring points. Consequently, it concluded that the proposed method shows better prediction ability and accuracy than the SOA-LSTM model and the stepwise regression model. [ABSTRACT FROM AUTHOR]

Many studies have identified the influences of PM2.5. However, very little research has addressed the spatiotemporal dependence and heterogeneity in the relationships between impact factors and PM2.5. This study firstly utilizes spatial statistics and time series analysis to investigate the spatial and temporal dependence of PM2.5 at the city level in China using a three-year (2015–2017) dataset. Then, a new local regression model, multiscale geographically weighted regression (MGWR), is introduced, based on which we measure the influence of PM2.5. A spatiotemporal lag is constructed and included in MGWR to account for spatiotemporal dependence and spatial heterogeneity simultaneously. Results of MGWR are comprehensively compared with those of ordinary least square (OLS) and geographically weighted regression (GWR). Experimental results show that PM2.5 is autocorrelated in both space and time. Compared with existing approaches, MGWR with a spatiotemporal lag (MGWRL) achieves a higher goodness-of-fit and a more significant effect on eliminating residual spatial autocorrelation. Parameter estimates from MGWR demonstrate significant spatial heterogeneity, which traditional global models fail to detect. Results also indicate the use of MGWR for generating local spatiotemporal dependence evaluations which are conditioned on various covariates rather than being simple descriptions of a pattern. This study offers a more accurate method to model geographic events. [ABSTRACT FROM AUTHOR]

In this article, geovisualization is used for the presentation and interpretation of spatial analysis results concerning several house attributes. For that purpose, point data for houses in the region of Attica, Greece are analyzed. The data concern houses for sale and comprise structural characteristics, such as size, age and floor, as well as locational attributes. Geovisualization of house characteristics is performed employing spatial interpolation techniques, kriging techniques, in particular. Spatial autocorrelation in the data is examined through the calculation of the Moran's I coefficient, while spatial clusters of houses with similar characteristics are identified using the Getis-Ord Gi* local spatial autocorrelation coefficient. Finally, a model is developed in order to predict house prices according to several structural and locational characteristics. In that respect, a classic hedonic pricing model is constructed, which is consequently developed as a geographically weighted regression (GWR) model in a GIS environment. The results of this model indicate that two characteristics, i.e., size and age, account for most of the variability in house prices in the study region. Since GWR is a local model producing different regression parameters for each observation, it is possible to obtain the spatial distribution of the regression parameters, which indicate the significance of the house characteristics for price determination in different locations in the study area. [ABSTRACT FROM AUTHOR]

Spatial regression models have recently received a lot of attention in a variety of fields to address the spatial autocorrelation effect. One important class of spatial models is the Conditional Autoregressive (CA). Theses models have been widely used to analyze spatial data in various areas, as geography, epidemiology, disease surveillance, civilian planning, mapping of poorness signals and others. In this article, we propose the Liu-type pretest, shrinkage and positive shrinkages estimators for the large-scale effect parameter vector of the CA regression model. The set of the proposed estimators are evaluated analytically via their asymptotic bias, quadratic bias, the asymptotic quadratic risks, and numerically via their relative mean squared errors. Our results demonstrate that the proposed estimators are more efficient than Liu-type estimator. To conclude this paper, we apply the proposed estimators to the Boston housing prices data, and applied a bootstrapping technique to evaluate the estimators based on their mean squared prediction error. [ABSTRACT FROM AUTHOR]

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The urban structure of a city, defined by its inhabitants, daily movements, and land use, has become an environmental factor of interest that is related to traffic accidents. Traditionally, macro modeling is usually implemented using spatial econometric methods; however, techniques such as support vector regression have proven to be efficient in identifying the relationships between factors at the zonal level and the frequency associated with these events when considering the autocorrelation between spatial units. As a result of this, the main objective of this study was to evaluate the impact of socioeconomical, land use, and mobility variables on the frequency of traffic accidents through the analysis of area data using spatial and vector support regression models. The spatial weighting matrix term was incorporated into the support vector regression models to compare the results against those that ignore it. The urban land of Bogotá, disaggregated into the territorial units of mobility analysis, was delimited as a study area. Two response variables were used: the traffic accidents index on the road perimeter and the traffic accidents index with deaths on the road perimeter, to analyze the total number of traffic accidents and the deaths caused by them. The results indicated that the rate of trips per person by taxi and motorcycle had the greatest impact on the increase in total accidents and deaths caused by them. Support vector regression models that incorporate the spatial structure allowed the modeling of the spatial dependency between spatial units with a better fit than the spatial regression models. [ABSTRACT FROM AUTHOR]

We seek to use ARM MJO Investigation Experiment (AMIE)-DYNAMO field campaign observations to significantly constrain height-resolved estimates of the parameterization-relevant, causal sensitivity of convective heating Q to water vapor q. In field data, Q profiles are detected via Doppler radar wind divergence D while balloon soundings give q. Univariate regressions of D on q summarize the information from a 10-layer time–pressure series from Gan Island (0°, 90°E) as a 10 × 10 matrix. Despite the right shape and units, this is not the desired causal quantity because observations reflect confounding effects of additional q-correlated casual mechanisms. We seek to use this matrix to adjudicate among candidate estimates of the desired causal quantity: Kuang's matrix of the linear responses of a cyclic convection-permitting model (CCPM) at equilibrium. Transforming to more observation-comparable forms by accounting for observed autocorrelations, the comparisons are still poor, because (we hypothesize) larger-scale vertical velocity, forbidden by CCPM methodology, is another confounding cause that must be permitted to covary with q. By embedding and modified candidates in an idealized GCM, and treating its outputs as virtual field campaign data, we find that observations favor a factor of 2 (rather than 0 or 1) to small-domain 's free-tropospheric causal q sensitivity of about 25% rain-rate increment over 3 subsequent hours per +1 g kg−1q impulse in a 100-hPa layer. Doubling this sensitivity lies partway toward Kuang's for a long domain that organizes convection into squall lines, a weak but sign-consistent hint of a detectable parameterization-relevant (causal) role for convective organization in nature. Caveats and implications for field campaign proposers are discussed. [ABSTRACT FROM AUTHOR]

Most theoretical studies have explored the evolution of plasticity when the environment, and therefore the optimal trait value, varies in time or space. When the environment varies in time and space, we show that genetic adaptation to Markovian temporal fluctuations depends on the between‐generation autocorrelation in the environment in exactly the same way that genetic adaptation to spatial fluctuations depends on the probability of philopatry. This is because both measure the correlation in parent‐offspring environments and therefore the effectiveness of a genetic response to selection. If the capacity to genetically respond to selection is stronger in one dimension (e.g., space), then plasticity mainly evolves in response to fluctuations in the other dimension (e.g., time). If the relationships between the environments of development and selection are the same in time and space, the evolved plastic response to temporal fluctuations is useful in a spatial context and genetic differentiation in space is reduced. However, if the relationships between the environments of development and selection are different, the optimal level of plasticity is different in the two dimensions. In this case, the plastic response that evolves to cope with temporal fluctuations may actually be maladaptive in space, resulting in the evolution of hyperplasticity or negative plasticity. These effects can be mitigated by spatial genetic differentiation that acts in opposition to plasticity resulting in counter‐gradient variation. These results highlight the difficulty of making space‐for‐time substitutions in empirical work but identify the key parameters that need to be measured in order to test whether space‐for‐time substitutions are likely to be valid. [ABSTRACT FROM AUTHOR]

Series models have several functions: comprehending the functional dependence of variable of interest on covariates, forecasting the dependent variable for future values of covariates and estimating variance disintegration, co-integration and steady-state relations. Although the regression function in a time series model has been extensively modeled both parametrically and nonparametrically, modeling of the error autocorrelation is mainly restricted to the parametric setup. A proper modeling of autocorrelation not only helps to reduce the bias in regression function estimate, but also enriches forecasting via a better forecast of the error term. In this article, we present a nonparametric modeling of autocorrelation function under a Bayesian framework. Moving into the frequency domain from the time domain, we introduce a Gaussian process prior to the log of the spectral density, which is then updated by using a Whittle approximation for the likelihood function (Whittle likelihood). The posterior computation is simplified due to the fact that Whittle likelihood is approximated by the likelihood of a normal mixture distribution with log-spectral density as a location shift parameter, where the mixture is of only five components with known means, variances, and mixture probabilities. The problem then becomes conjugate conditional on the mixture components, and a Gibbs sampler is used to initiate the unknown mixture components as latent variables. We present a simulation study for performance comparison, and apply our method to the two real data examples. [ABSTRACT FROM AUTHOR]

Power output from photovoltaic (PV) systems in outdoor conditions is substantially influenced by climatic parameters such as solar irradiance and temperature. PV manufacturers always provide technical specifications in laboratory conditions but reliable relationship for the power output must be determined for accurate prediction under real operating conditions. For the present study, solar irradiance G, temperature T and electrical power output P data under real conditions are methodically and regularly inscribed in dataloggers. Hence, in this paper, we investigate rigorous and robust statistical methods for small sample such as Augmented Dickey- Fuller and Engle Granger for stationary series to determine the estimate regression between variables P, G & T. A first regression of power output P time series variable on solar irradiance G time series has shown spurious results and thus spurious regression. The first differences of such time series are stationary and a regression is proposed whereas temperature variable is identified as not significant and where autocorrelation of residuals is suspected. Finally, the novelty of this paper is the Engle & Granger method that is used to provide a relationship between variables P and G in a difference level. A final relationship without suspicious heteroscedasticity has been determined. Our model is formulated on the basis of PV real conditions statistical approach and is more realistic than steady approach models. [ABSTRACT FROM AUTHOR]

This study entails generation of empirical ordinary least squares regression models to estimate water parameters. It uses remote sensing for environmental monitoring of Pasig River located in the Philippines. This uses measurements of primary water quality (WQ) parameters defined on Department of Environment and Natural Resources Administrative Order 2016-08 recorded on the Pasig River Unified Monitoring Stations (PRUMS) report from January to June of 2019. Sentinel-2 images are utilized to estimate biological oxygen demand (BOD), Chloride, Color, Dissolved Oxygen (DO), Fecal Coliform, Nitrate, pH, Phosphate, Temperature, and Total suspended solids (TSS). Feature generation involved calculation of different band reflectances from the satellite image. Exhaustive feature selection through application of a Pearson Correlation threshold was applied to limit number of independent variables. The box-cox transformations of water quality parameters (except for Temperature) were used as dependent variables and the selected features are used as dependent variables for the ordinary least squares regression model. The root mean square error (RMSE) values for the models which are computed using the k-fold cross validation technique showed outliers, especially for the TSS model (>547000 mg/L), which made its average negative RMSE so large. Tests for multicollinearity, autocorrelation, and homoscedasticity indicated problems in models created. However, normality of residuals indicates that models allow us to roughly estimate water quality for the river as a whole with the advantages of remote sensing, enabling a better perspective for its spatial distribution. [ABSTRACT FROM AUTHOR]

Rapid urbanization in China has led to an excessive urban expansion of built-up areas, which makes quantitative research on compact city important. We adopted density and the degree of mixed land use to measure the compactness of 160 Chinese cities. Spatial autocorrelation analysis was performed to identify spatial clustering patterns, and the relationships between compactness and five variables were explored through regression models. The result shows that in nearly half of the cases, the calculated values of two indices are less than the average. The high or low values of density and the degree of mixed land use tend to be spatially clustered. The hot spot regions of density and the degree of mixed land use lie mainly in the south of China, while the north present as cold spots or the insignificant regions. Urban compactness can be affected by multifaceted factors and the relationships between compactness and five variables are not consistent throughout the areas of analysis. The GWR model can identify this phenomenon and provides a better fit than the OLS model. This study proposed a new approach to measure the compactness, and the results of GWR analysis can conducive to appropriate policy-making based on different local conditions. [ABSTRACT FROM AUTHOR]

There is ongoing concern that concentrations of migrant ethnic minority groups could increase distrust within Australian communities. This article assesses whether the existence of an area of high migrant ethnic minority concentration associates with low propensity to trust. After identification using Local Spatial Autocorrelation Statistics and controlling for other variables, a regression technique indicated significantly low trust associated with this area in 2006. Further investigation shows that the association of low level of trust and the concentration area was not as severe in 2010 and had even smaller and insignificant association in 2005. [ABSTRACT FROM AUTHOR]

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson’s statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran’s index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China’s regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test. [ABSTRACT FROM AUTHOR]

The author discusses an article that has proposed an original method termed as spatial component regression (SCR) as means of addressing autocorrelation in linear regression models, which combines ordinary least squares (OLS) analysis with an ordination to identify and remove spatial patterns in the residuals. He mentions autocorrelation as a universal feature of spatial ecological data that has been the subject of cited review.

The aim of the study was to identify the hotspots of heavy metals in sediments for exploring possible sources and understand the relationship between heavy metals and environmental variables. West Chaohu Lake was selected as the study area, 38 surface sediment samples and 4 river mouth sediment samples were obtained and analysed for three typical metals (i.e. Co, Mn and Pb). Local indicators spatial associate (LISA) analysis detected spatial clusters and spatial outliers of enrichment factor (EF) values of the three metals and found the samples with pollution belong to high-high clusters, low-low clusters, even low-high outliers. Geostatistics and local Moran’s I were combined, and the results indicated that Co is mainly from natural sources, Mn is influenced by upward migration and reprecipitation, and Pb is influenced by anthropogenic sources. Furthermore, Pb was chosen as an example to understand the relationship between heavy metals and environmental variables. Compared to ordinary least squares (OLS) model, spatial autoregressive regression (SAR) model performed better and accounted for the phenomenon of spatial autocorrelation. Grain particle percent, loss on ignition (LOI), distance to Nanfei River mouth has a significant influence on the variation of Pb concentrations in sediments. Hotspots identification and spatial regression analysis can play an important role in understanding the pollution process for pollution management and restoration. [ABSTRACT FROM AUTHOR]

The prevailing view in finance is that the evidence for long-horizon stock return predictability is significantly stronger than that for short horizons. We show that for persistent regressors, a characteristic of most of the predictive variables used in the literature, the estimators are almost perfectly correlated across horizons under the null hypothesis of no predictability. For the persistence levels of dividend yields, the analytical correlation is 99% between the 1- and 2-year horizon estimators and 94% between the 1- and 5-year horizons. Common sampling error across equations leads to ordinary least squares coefficient estimates and R²s that are roughly proportional to the horizon under the null hypothesis. This is the precise pattern found in the data. We perform joint tests across horizons for a variety of explanatory variables and provide an alternative view of the existing evidence. [ABSTRACT FROM AUTHOR]

Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of Yule (1926) and Granger and Newbold (1974). Data mining for predictor variables interacts with spurious regression bias. The two effects reinforce each other, because more highly persistent series are more likely to be found significant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious. [ABSTRACT FROM AUTHOR]

It has long been known that insufficient consideration of spatial autocorrelation leads to unreliable hypothesis-tests and inaccurate parameter estimates. Yet, ecologists are confronted with a confusing array of methods to account for spatial autocorrelation. Although Beale et al. (2010) provided guidance for continuous data on regular grids, researchers still need advice for other types of data in more flexible spatial contexts. In this paper, we extend Beale et al. (2010)'s work to count data on both regularly- and irregularly-spaced plots, the latter being commonly encountered in ecological studies. Through a simulation-based approach, we assessed the accuracy and the type I errors of two frequentist and two Bayesian ready-to-use methods in the family of generalized mixed models, with distance-based or neighbourhood-based correlated random effects. In addition, we tested whether the methods are robust to spatial non-stationarity, and over- and under-dispersion - both typical features of species distribution count data which violate standard regression assumptions. In the simplest of our simulated datasets, the two frequentist methods gave inflated type I errors, while the two Bayesian methods provided satisfying results. When facing real-world complexities, the distance-based Bayesian method (MCMC with Langevin-Hastings updates) performed best of all. We hope that, in the light of our results, ecological researchers will feel more comfortable including spatial autocorrelation in their analyses of count data. [ABSTRACT FROM AUTHOR]

The modified autoregressive (mAR) index has been proposed as a description of the clustering of shots of similar duration in a motion picture. In this paper we derive robust estimates of the mAR index for high grossing films at the US box office using a rank-based autocorrelation function resistant to the influence of outliers and compare this to estimates obtained using the classical, moment-based autocorrelation function. The results show that (1) The classical mAR index underestimates both the level of shot clustering in a film and the variation in style among the films in the sample; (2) there is a decline in shot clustering from 1935 to the 1950s followed by an increase from the 1960s to the 1980s and a levelling off thereafter rather than the monotonic trend indicated by the classical index, and this is mirrored in the trend of the median shot lengths and interquartile range; and (3) the rank mAR index identifies differences between genres overlooked when using the classical index. [ABSTRACT FROM AUTHOR]

Correctly assessing a scientist's past research impact and potential for future impact is key in recruitment decisions and other evaluation processes. While a candidate's future impact is the main concern for these decisions, most measures only quantify the impact of previous work. Recently, it has been argued that linear regression models are capable of predicting a scientist's future impact. By applying that future impact model to 762 careers drawn from three disciplines: physics, biology, and mathematics, we identify a number of subtle, but critical, flaws in current models. Specifically, cumulative non-decreasing measures like the h-index contain intrinsic autocorrelation, resulting in significant overestimation of their "predictive power''. Moreover, the predictive power of these models depend heavily upon scientists' career age, producing least accurate estimates for young researchers. Our results place in doubt the suitability of such models, and indicate further investigation is required before they can be used in recruiting decisions. [ABSTRACT FROM AUTHOR]

A Gaussian integer is a complex number whose real and imaginary parts are both integers. Meanwhile, a sequence is defined as perfect if and only if the out-of-phase value of the periodic autocorrelation function is equal to zero. This paper presents two novel classes of perfect sequences constructed using two groups of base sequences. The nonzero elements of these base sequences belong to the set \\ ± 1,\ ± j\. A perfect sequence can be obtained by linearly combining these base sequences or their cyclic shift equivalents with arbitrary nonzero complex coefficients of equal magnitudes. In general, the elements of the constructed sequences are not Gaussian integers. However, if the complex coefficients are Gaussian integers, then the resulting perfect sequences will be Gaussian integer perfect sequences (GIPSs). In addition, a periodic cross-correlation function is derived, which has the same mathematical expression as the investigated sequences. Finally, the maximal energy efficiency of the proposed GIPSs is investigated. [ABSTRACT FROM PUBLISHER]

To explore the relationship between the geographical factors and P-R interval reference value of healthy adults, and to elucidate the geographical distribution of the P-R interval reference value in Chinese adults, to provide the basis for the standard of P-R interval reference value in different regions. The P-R interval reference value of 64,753 healthy adults in 341 cities (counties) level hospitals, research institutes, and universities in China was collected, and 15 geographical data and P-R interval reference value of healthy people were studied, and then 10 geographical factors with correlation were extracted for further analysis. Using spatial autocorrelation analysis to determine the autocorrelation of data space, using backward regression and ridge regression to construct forecast model, and the optimal prediction model is selected by comparing and evaluating the prediction effect of each model, the spatial distribution map of P-R interval reference value of Chinese healthy adults was constructed by statistical analysis. There was a significant correlation between normal reference value of adult P-R interval reference value and geographical factors. The regression model of normal reference value and geographical factors of adult P-R interval reference value was established by ridge regression analysis method: Y ̂ =149.2 + 0.02590X2 + 0.0005000X3 + 0.02634X5–0.05890X6 + 0.0008400X7 + 0.01606X8 + 0.2592X9–0.03638X12 + 5.888X13–0.2126X15 ± 8.6, and ArcGIS10.2. In the software interpolation method, the distribution chart of normal reference value of Chinese adult P-R interval reference value was inserted. Knowing the geographical factors of some places in China, we can use this model to estimate the P-R interval reference value in this region, and to obtain the normal P-R interval reference value between adults anywhere in China. [ABSTRACT FROM AUTHOR]

Vertical gradients (e.g., water temperature and density) are a measurable expression of the potential energy of a water body, which is a fundamental driver for biogeochemical processes in aquatic ecosystems. Seasonal vertical stratification is typically described by the mixed layer and thermocline depths, and these metrics are often estimated through visual assessment of graphical plots or using numerical methods. The most widely used numerical method estimates the derivative of temperature (or density) along the depth, but it is sensitive both to profile data resolution and presence of nonconforming observations. In this study, we propose a new method of modeling vertical gradients using a four‐parameter sigmoidal function, including temporal autocorrelation. The parameters were estimated through Bayesian nonlinear regression with conditional autoregressive errors. The proposed method provides a quantitative and automated way to estimate the mixed layer and thermocline depths even for data profiles with a poor resolution. It also showed good performance against high‐frequency measurement data. [ABSTRACT FROM AUTHOR]

Trade winds localized within the Western Equatorial Pacific express lagged and statistically significant correlations to sunspot numbers as well as to streamflow in rivers of the Southern Rocky Mountains. Both correlation sets were integrated in a linear regression analysis to produce relatively accurate sub-decadal streamflow forecasts for an annual and a 5-year average. In comparison to the autocorrelation technique, the prototyped method yielded the highest correlations, the highest goodness-of-fit scores, and the lowest root mean squared errors, for both the 5-year average and the annual average assignments. Of all of the cases examined, the highest Kolmogorov-Smirnov test scores between observation and prediction were found for the single solar-based forecast 5 years in advance for the 60-month average streamflow of the Animas River in New Mexico. [ABSTRACT FROM AUTHOR]

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]

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]

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.

The authors briefly review the literature associated with the autocorrelated current effects model and present a simple procedure that recovers its parameters from time-aggregated data when the level of aggregation is known. The procedure is based partly on the estimation of a first-order autocorrelation coefficient. The procedure is illustrated and its properties are compared with those of a GLS procedure by means of a Monte Carlo experiment. In many of the tested cases, there is good recovery of the microparameters with aggregated data. [ABSTRACT FROM AUTHOR]

Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently are.widely covered in the literature, only sparse attention is giver to their integration. This paper focuses on the interplay between RSA and the spatial scaling of species-environment relationships. Using a hypothetical species in an artificial landscape, we show that a misipatch between the scale of analysis and the scale of a species' response to its environment leads to a decrease in the portion of variation explained by environmental predictors. Moreover, it results in RSA and biased regression coefficients. This bias stems from error-predictor dependencies due to the scale mismatch, the magnitude of which depends on the interaction between the scale of landscape heterogeneity and the scale of a species' response to this heterogeneity. We show that explicitly considering scale effects on RSA can reveal the characteristic scale of a species' response to its environment. This is important, because the estimation of species-environment relationships using spatial regression methods proves to be erroneous in case of a scale mismatch, leading to spurious conclusions when scaling issues are not explicitly considered. The findings presented here highlight the importance of examining the appropriateness of the spatial scales used in analyses, since scale mismatches affect the rigor of statistical analyses and thereby the ability to understand the processes underlying spatial patterning in ecological phenomena. [ABSTRACT FROM AUTHOR]

Abstract: The effect of foot traffic on indoor particle resuspension was evaluated by associating non-prescribed foot traffic with simultaneous size-resolved airborne particulate matter (PM) concentrations in a northern California hospital. Foot traffic and PM were measured every 15 min in a carpeted hallway over two 27-h periods. The PM concentration in the hallway was modeled based on the foot traffic intensity, including the previous PM concentration via an autocorrelation regression method based on the well-mixed box model. All 5 size ranges of PM, ranging from 0.75–1 μm to 5–7.5 μm, were highly correlated with foot traffic measurements for both monitoring periods (p < 0.001, R 2 = 0.87–0.90). However, correlations during daytime hours were less significant than nighttime. Coefficients found via this autoregressive analysis can be interpreted to reveal (i) time-independent contributions of walking activities on PM levels for a specific location; and (ii) size-specific characteristics of the resuspended PM. [Copyright &y& Elsevier]

This paper discusses the estimation of autocorrelation function (ACF) of fractional Gaussian noise (fGn) with long-range dependence (LRD). A variance bound of ACF estimation of one block of fGn with LRD for a given value of the Hurst parameter (H) is given. The present bound provides a guideline to require the block size to guarantee that the variance of ACF estimation of one block of fGn with LRD for a given H value does not exceed the predetermined variance bound regardless of the start point of the block. In addition, the present result implies that the error of ACF estimation of a block of fGn with LRD depends only on the number of data points within the sample and not on the actual sample length in time. For a given block size, the error is found to be larger for fGn with stronger LRD than that with weaker LRD. [ABSTRACT FROM AUTHOR]

For uniform frequency stepped pulse trains, there can be undesirable peaks of the autocorrelation function, known as "grating lobes." In this paper we address this issue, using an approach which allows us to suppress grating lobes in the case of appropriately chosen stepped-frequency waveforms, i.e., sequences of narrowband pulses that span the desired bandwidth. We introduce a multiparametric generalization of a stepped-frequency train, and by exploiting a factorization of the autocorrelation function, will be able to effect a useful tradeoff between competing properties of the factors by careful choices of relevant parameters. We provide a detailed description of the method applied to a representative data set. [ABSTRACT FROM AUTHOR]

Abstract: In this paper chaotic behavior in the nitrogen dioxide and sulphurous anhydride concentration time series at two sites in Gdansk region is investigated. To reconstruct an attractor, the time delay and embedding dimension are needed. The former is determined by the methods of autocorrelation function and average mutual information, and the latter is calculated by means of correlation dimension method and algorithm of false nearest neighbors. It was shown that the low-dimensional chaos existed in the time series under investigation. The spectrum of Lyapunov exponents was reconstructed as well as both Kaplan–Yorke dimension and Kolmogorov entropy that inversely proportional to the predictability limit are calculated. Non-linear prediction method is used for the time series. It is shown that even though the simple procedure is used to construct the non-linear model, the results are quite satisfactory. [Copyright &y& Elsevier]

In 1999, Davis and Jedwab gave an explicit algebraic normal form for m! · 2h(m+2) ordered Golay pairs of length 2m over Z2h, involving m!/2 · 2h(m+2) Golay sequences. In 2095, Li and Chu unexpectedly found an additional 1024 length 16 quaternary Golay sequences. Fiedler and Jedwab showed in 2006 that these new Golay sequences exist because of a "crossover" of the aperiodic autocorrelation function of certain quaternary length eight sequences belonging to Golay pairs, and that they spawn further new quaternary Golay sequences and pairs of length 2m for m > 4 under Budišin's 1990 iterative construction. The total number of Golay sequences and pairs spawned in this way is counted, and their algebraic normal form is given explicitly. A framework of constructions is derived in which Thryn's 1974 product construction, together with several variations, plays a key role. All previously known Golay sequences and pairs of length 2m over Z2h can be obtained directly in explicit algebraic normal form from this framework. Furthermore, additional quaternary Golay sequences and pairs of length 2m are produced that cannot be obtained from any other known construction. The framework generalizes readily to lengths that are not a power of 2, and to alphabets other than Z2h. [ABSTRACT FROM AUTHOR]

I provide a brief introduction to the concept of spatial autocorrelation and its incorporation into regression- type models. Spatial autocorrelation occurs when the response variable is correlated with itself at other locations in the region of interest. The autocorrelation usually takes a specific form where observations close in space are more correlated than those farther apart, and the rate of decay of the correlation is a function of the distance separating 2 locations. I present 2 commonly used models: 1) geostatistical modeling in which data are collected at points in the study region and 2) conditional autoregression (lattice) models in which data are aggregated over small nonoverlapping sub-areas of the study region. I also describe incorporation of explanatory covariates, such as habitat or physico- chemical attributes. I emphasize frequentist methods, but I briefly describe Bayesian approaches. I also provide some advantages, such as obtaining correct standard errors for estimators, and disadvantages, such as requirements for larger sample sizes, of incorporating spatial autocorrelation into the modeling effort. This information can aid researchers in designing and analyzing models of the relationships between species distributions and habitat. As a result, more informative models can be developed which further aid in management of wildlife. [ABSTRACT FROM AUTHOR]

Branch-and-bound uses relaxation to prune search trees but sometimes scales poorly to large problems. Conversely, local search often scales well but may be unable to find optimal solutions. Both phenomena occur in the construction of low-autocorrelation binary sequences (LABS), a problem arising in communication engineering. This paper proposes a hybrid approach to optimization: using relaxation to prune local search spaces. An implementation gives very competitive results, showing the feasibility of the approach. [ABSTRACT FROM AUTHOR]

Abstract: Let p be a prime, and be the finite field with q elements. In this paper, we will consider q-ary sequences of period for and study their various balance properties: symbol-balance, difference-balance, and two-tuple-balance properties. The array structure of the sequences is introduced, and various implications between these balance properties and the array structure are proved. Specifically, we prove that if a q-ary sequence of period is difference-balanced and has the “cyclic” array structure then it is two-tuple-balanced. We conjecture that a difference-balanced q-ary sequence of period must have the cyclic array structure. The conjecture is confirmed with respect to all of the known q-ary sequences which are difference-balanced, in particular, which have the ideal two-level autocorrelation function when . [Copyright &y& Elsevier]

First I derive the power spectrum (of the cumulative or integrated level density) for the regular (Poissonian) energy spectrum which is 1/t2, and for the fully chaotic one, which is 1/t, as was observed numerically by Relano et al. [2002], and recently discussed independently by Faleiro et al. [2004]. The statement refers to small values of t ≤ 1, i.e. for times smaller than Heisenberg time. For t ≫ 1 it is always 1/t2. Then I analyze the autocorrelation function for spectrum which is a superposition of statistically independent spectral sequences and derive the exact additivity formula for the autocorrelation function, and consequently for the form factor of the density of energy levels and of the cumulative (integrated) density of energy levels (the form factor is the Fourier transform of the corresponding autocorrelation function). Therefore this theory provides prediction for the deep semiclassical regime of sufficiently small effective Planck constant ℏeff. However, in not sufficiently deep semiclassical regime (not sufficiently small ℏeff) we see a deviation from this behavior, as recently demonstrated numerically by Gomez et al. [2004] in a billiard system [Robnik, 1983] where a power law behavior 1/tα is found, and the exponent α goes continuously from 2 to 1, as the system's dynamics goes from complete integrability (Poisson) to full chaoticity (GOE and GUE), respectively. [ABSTRACT FROM AUTHOR]

It has been suggested that the dorsal cochlear nucleus (DCN) is involved in the temporal representation of both envelope periodicity and pitch. This hypothesis is tested using iterated rippled noise (IRN), which is generated by a cascade of delay and add [IRN(+)] or delay and subtract [IRN(-)] operations. The autocorrelation functions (ACFs) of the waveform and the envelope of IRN(+) have a first peak at the delay, which corresponds to the perceived pitch of the IRN. With the same delay, the pitch of IRN(-) is generally an octave lower than for IRN(+). This is reflected in a first peak at twice the delay in the ACF of the waveform for IRN(-). In contrast, for identical delays, the ACF of the envelope for both IRN(-) and IRN(+) is the same. Thus the use of IRN allows the distinction between envelope- or fine-structure sensitivity. Recordings were made from 135 single units (BFs <5 kHz) in the DCN of the anesthetized guinea pig using IRN with delays ranging from 1 to 32 ms. In our sample 42% were sensitive to the periodicity of IRN(+) and were tuned to a particular delay in their first-order interspike interval histograms (ISIHs). This tuning was highly correlated with their response to white noise. Most units with best frequencies (BFs) <500 Hz show a different all-order ISIH for IRN(+) and IRN(-), which corresponds to the perceived pitch difference, whereas units with higher BFs show a similar response to IRN(+) and IRN(-). The results indicate that low-frequency units (BF <500 Hz) in the DCN may be involved in the representation of the waveform fine structure, although units with BFs >500 Hz are able to encode only the envelope periodicity of broadband IRN in their temporal discharge characteristics. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology for simulation of binary random fields according to their prescribed autocorrelation function. It starts with a brief outline of the essential features of binary random fields and their implications in modeling two-phase random media. The exposition of the proposed methodology is done in two steps. In the first step, an algorithm is introduced to obtain samples of a binary field from generated realizations of a Gaussian field, using the theory of zero crossings of Gaussian fields. This mapping constitutes essentially a nonlinear transformation with memory of the Gaussian sample functions. In the second step, an iterative algorithm is introduced that allows the determination of the probabilistic characteristics of the underlying Gaussian field, so that the resulting binary field obtained through the proposed nonlinear transformation has a prescribed autocorrelation function. Several numerical examples are provided to demonstrate the capabilities of the methodology, especially in modeling two-phase random media. The methodology is shown to have a wide range of applicability and its computational cost is small, especially when a large number of realizations is needed. [ABSTRACT FROM AUTHOR]

Presents a simple analytic expression for the ambiguity function and autocorrelation function of bandwidth of a coherent pulse-train. Derivation of a simple relationship between a frequency step, bandwidth and pulse-duration from the pulse-train; Effective way to increase the bandwidth of a pulse-train; Implication of a large frequency step.

A new portmanteau test for time series more powerful than the tests of Ljung and Box and Monti, is proposed. The test is based on the mth root of the determinant of the mth autocorrelation matrix. It is shown that the proposed statistic is a function of all of the squared multiple correlation coefficients of the regressions of the residuals on their lags when the number of lags goes from 1 to m. It can also be written us a function of the first m partial autocorrelation coefficients. The asymptotic distribution of the test statistic is a linear combination of chi-squared distributions and can be approximated by a gamma distribution. It is shown, depending on the model and sample site, than this test can be up to 50% more powerful than the Ljung and Bux and Monti tests. The test is applied to the detection of several types of nonlinearity by using the autocorrelation matrix of the squared residuals, and it is shown that, in general. the new test is more powerful than the test of McLeod and Li. An example is presented in which this test finds nonlinearity in the residuals of the sunspot series. [ABSTRACT FROM AUTHOR]

Suggests that spatial autocorrelation tests on residuals provide a computationally feasible approach to selecting model complexity. Function of residual spatial autocorrelation (RSA); Performance of the RSA criterion for smoothing parameter selection in univariate regression; Description of a possible modification of the method for non-iid errors having short-range correlations.

Longitudinal survey data may comprise ordinal polytomous repeated observations and a set of multidimensional covariates for a large number of individuals. One of the main goals of the longitudinal survey is then to describe the marginal expectation of the ordinal polytomous outcome variable as a function of the covariates while accounting for the structural as well as longitudinal correlations. The structural correlations come from the polytomous nature of the response variable, and the longitudinal correlations from the repetition of the polytomous responses over time. In this paper we develop a generalised estimating equations approach based on autocorrelation structure to deal with multivariate polytomous longitudinal survey data, the univariate and bivariate analyses being special cases. The regression estimators are shown to be consistent for the corresponding regression parameters. The methods are illustrated by using the Survey of Labour and Income Dynamics data from Statistics Canada. [ABSTRACT FROM PUBLISHER]

For regularly-spaced observations with the Gaussian autocorrelation function, the finite and infinite autoregressive and moving-average representations can be obtained theoretically. This allows exact Gaussian maximum likelihood to be performed very accurately, and gives a simple exact simulation method. [ABSTRACT FROM PUBLISHER]

This paper develops a practical approach to diagnosing the existence of a latent stochastic process in the mean of a Poisson regression model. The asymptotic distribution of standard generalised linear model estimators is derived for the case where an autocorrelated latent process is present. Simple formulae for the effect of autocovariance on standard errors of the regression coefficients are also provided. Methods for adjusting for the severe bias in previously proposed estimators of autocovariance are derived and their behaviour is investigated. Applications of the methods to time series of monthly polio counts in the USA and daily asthma presentations at a hospital in Sydney are used to illustrate the results and methods. [ABSTRACT FROM PUBLISHER]

Spatiotemporal nonstationarity and autocorrelation are two crucial points in modeling geographical data. Previous studies have demonstrated that geographically and temporally weighted autoregressive (GTWAR) model accounts for both spatiotemporal nonstationarity and autocorrelation simultaneously to estimate house prices. Therefore, this paper proposes a kernel-based GTWAR (KBGTWAR) model by incorporating the basic principle of support vector machine regression into spatially and temporally varying coefficients model. The efficacy of KBGTWAR model is demonstrated through a case study on housing prices in the city of Shenzhen, China, from year 2004 to 2008. Comparing the existing models, KBGTWAR model obtains the lowest value for the residual sum of squares (RSS) and the highest value for the coefficient of determination R2. Moreover, KBGTWAR model improves the goodness of fit of the existing GTWAR model from 12.0 to 4.5 in terms of RSS, from 0.914 to 0.968 in terms of R2 and from 3.84 to 4.45 in terms of F-statistic. The results show that KBGTWAR model provides a comparatively high goodness of fit and sufficient explanatory power for both spatiotemporal nonstationarity and autocorrelation. The results of this study demonstrate that the proposed KBGTWAR model can be used to effectively formulate polices for real estate management. [ABSTRACT FROM AUTHOR]