Comparing of Aviation Network Structure of Mid-south,Northwest and Southwest of China Based on Hierarchical Index Model

In order to compare the aviation network of mid-south,northwest and southwest of China to reveal the structure similarity and difference for providing quantitative evidence to construct regional aviation network and improve its structure,hierarchical index model of regional aviation network was established through dividing the aviation network into layers to research its structure characters.Data matrixes were defined to record the basic state of regional aviation network.Index matrixes were constructed to describe the quantitative features of regional aviation network.On the basis of these indexes,several structure indexes of all layers of aviation network were calculated to show the structure features of aviation network,such as ratio of passenger volume within the region with across the region,share rate of passenger volume among layers,ratio of average number of airline for each airport,ratio of average passenger volume for each airline and ratio of airline rate.According to the statistical data,similar structure of share rate of passenger volume among layers and average passenger volume for each airline in their regional aviation network was found after calculating.But on the side of ratio of passenger volume within the region with across the region,ratio of average number of airlines for each airport and ratio of airline rate were different.

Statistical Diagnostic for Varying-Coefficient Single-Index Models Based on Empirical Likelihood Method

Varying-coefficient single-index model( VCSIM) avoids the so-called "curse of dimensionality " and is flexible enough to include several important statistical models. This paper considers statistical diagnosis for VCSIM. First,the parametric estimation equation is established based on empirical likelihood. Then,some diagnosis statistics are defined. At last, an example is given to illustrate all the results.

Variable Selection of Partially Linear Single-index Models

In this article, we study the variable selection of partially linear single-index model(PLSIM). Based on the minimized average variance estimation, the variable selection of PLSIM is done by minimizing average variance with adaptive l1 penalty. Implementation algorithm is given. Under some regular conditions, we demonstrate the oracle properties of aLASSO procedure for PLSIM. Simulations are used to investigate the effectiveness of the proposed method for variable selection of PLSIM.

Importance of Weighting for Multi-Variable Habitat Suitability Index Model: A Case Study of Winter- Spring Cohort of Ommastrephes bartramii in the Northwestern Pacific Ocean

Weighting values for different habitat variables used in multi-factor habitat suitability index (HSI) modeling reflect the relative influences of different variables on distribution of fish species. Using the winter-spring cohort of neon flying squid (Ommastrephes bartramii) in the Northwestern Pacific Ocean as an example, we evaluated the impact of different weighting schemes on the HSI models based on sea surface temperature, gradient of sea surface temperature and sea surface height. We compared differences in predicted fishing effort and HSI values resulting from different weighting. The weighting for different habitat variables could greatly influence HSI modeling and should be carefully done based on their relative importance in influencing the resource spatial distribution. Weighting in a multi-factor HSI model should be further studied and optimization methods should be developed to improve forecasting squid spatial distributions.

ASYMPTOTIC PROPERTIES OF ESTIMATORS IN PARTIALLY LINEAR SINGLE-INDEX MODEL FOR LONGITUDINAL DATA

In this article, a partially linear single-index model for longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be estimated simultaneously by the proposed method while the feature of longitudinal data is considered. The existence, strong consistency and asymptotic normality of the estimators are proved under suitable conditions. A simulation study is conducted to investigate the finite sample performance of the proposed method. Our approach can also be used to study the pure single-index model for longitudinal data.

HABITAT SUITABILITY INDEX MODELS: GREY HERON NESTING IN ZHALONG NATIONAL NATURE RESERVE

Grey heron (Ardea cimerca) is one kind of the great birds which are often seen in the northeast marsh area of P.R.China, and there are many grey herons to reproduce in Zhalong Nature Reserve from March to August annually. In this paper, through the inveingation of the grey herons nesting habitat and according to the water depth, vegetation type, cover density and plan heigh of the nesting place, the grey heron’s nesting habitat suitability index medes are established. The main model is s=(s1xs2xs3xs4)1/4,where s1 is the water depth suitability index, s2 is the vegetation type suitability index, s3 is the cover density index, sa is the plant height suitability index. These models provide a kind of reliable method for evaluating the habitat quality of the grey heron’s nesting.

An analysis of single-index model with monotonic link function

有单音的补品连接功能的单个索引的模型被调查。第一，连接工作，这被显示出 h (·) 能被一个图形的方法看。也就是说有水平轴上的适合的反应的阴谋；垂直的轴上的观察 y 能被用来设想连接功能。甚至当连接功能不是单音的补品时，这条图形的途径也是适用的，这被指出。注意那许多存在非参量的光滑的 ers 能也被用来估计 h (·) 。因此，经由与惩罚功能最大化协变性功能的连接功能的我花键近似在现在的工作被调查。标准的一致性被构造。小模拟被执行证实在纸建议的途径的效率。

Development and Validation of National Cotton Cultivar Registration Index Model in Yangtze River Valley

[Objective] Based on the cotton variety high yielding potential, fiber quality traits, disease resistance, and early maturity characters, a cultivar registration index model was developed to simplify the tedious calculation process in national cotton registration procedure, and thus to enhance the practical application of cultivar registration index in cotton breeding and cotton recommending. [Method] By means of correlation analysis, partial correlation analysis and path analysis methods, the correlation of cotton main properties and their effects on cultivar registration index were explored using the dataset of national cotton regional trials in Yangtze River Valley during 1996-2013. The cultivar registration index model was constructed with stepwise regression statistical technique to ascertain the quantitative relationship of main characters with cultivar registration index, and the regional cotton trial dataset in 2013 was used to validate the model. [Result] Several characters with larger determinants to cultivar registration index were screened out, i.e. lint yield increase ratio,pro-frost yield ratio, verticillium wilt index, fiber strength, fusarium wilt index and micronaire value. The cultivar registration index model defined the functional relationship of cultivar registration index with the selected main characters, among which lint yield increase ratio, fiber strength and micronaire value contributed most to cultivar registration index. The model validation with regional cotton trials in 2013 indicated the root mean square error, RMSE was only 2.77, and the variation coefficient was 6.77%, which confirmed the model prediction effect was quite perfect.[Conclusion] The developed cultivar registration index model was reliable enough to simulate the complicated scoring system in cultivar registration procedure, also simplified cotton registration process, and enhanced the practicability of the cultivar registration index.

Finite Mixture of Heteroscedastic Single-Index Models

In many applications a heterogeneous population consists of several subpopulations. When each subpopulation can be adequately modeled by a heteroscedastic single-index model, the whole population is characterized by a finite mixture of heteroscedastic single-index models. In this article, we propose an estimation algorithm for fitting this model, and discuss the implementation in detail. Simulation studies are used to demonstrate the performance of the algorithm, and a real example is used to illustrate the application of the model.

The Prediction for the Consumer Price Index of Residents in Perspective of Time Series Method in Case of Chongqing

The consumer price index (CPI) measures the relative number of changes in the price level of consumer goods and services over time, reflecting the trend and degree of changes in the price level of goods and services purchased by residents. This article uses the ARMA model to analyze the fluctuation trend of the CPI (taking Chongqing as an example) and make short-term predictions. To test the predictive performance of the model, the observation values from January to December 2023 were retained as the reference object for evaluating the predictive accuracy of the model. Finally, through trial predictions of the data from May to August 2023, it was found that the constructed model had good fitting performance.

STGI:a spatio-temporal grid index model for marine big data

Marine big data are characterized by a large amount and complex structures,which bring great challenges to data management and retrieval.Based on the GeoSOT Grid Code and the composite index structure of the MongoDB database,this paper proposes a spatio-temporal grid index model(STGI)for efficient optimized query of marine big data.A spatio-temporal secondary index is created on the spatial code and time code columns to build a composite index in the MongoDB database used for the storage of massive marine data.Multiple comparative experiments demonstrate that the retrieval efficiency adopting the STGI approach is increased by more than two to three times compared with other index models.Through theoretical analysis and experimental verification,the conclusion could be achieved that the STGI model is quite suitable for retrieving large-scale spatial data with low time frequency,such as marine big data.

Empirical likelihood confidence regions of the parameters in a partially linear single-index model

In this paper, a partially linear single-index model is investigated, and three empirical log-likelihood ratio statistics for the unknown parameters in the model are suggested. It is proved that the proposed statistics are asymptotically standard chi-square under some suitable conditions, and hence can be used to construct the confidence regions of the parameters. Our methods can also deal with the confidence region construction for the index in the pure single-index model. A simulation study indicates that, in terms of coverage probabilities and average areas of the confidence regions, the proposed methods perform better than the least-squares method.

Estimator of a change point in single index models

This paper considers the problem of change point in single index models.In order to obtain asymptotically valid confidence intervals for the estimation of the change point,the convergence rate and asymptotic distribution of the change point estimate is studied.Some simulation results are presented which show that the numerical performance of our estimator is satisfactory.

The Adaptive LASSO Spline Estimation of Single-Index Model

In this paper, based on spline approximation, the authors propose a unified variable selection approach for single-index model via adaptive L1 penalty. The calculation methods of the proposed estimators are given on the basis of the known lars algorithm. Under some regular conditions, the authors demonstrate the asymptotic properties of the proposed estimators and the oracle properties of adaptive LASSO(aL ASSO) variable selection. Simulations are used to investigate the performances of the proposed estimator and illustrate that it is effective for simultaneous variable selection as well as estimation of the single-index models.

A distribution-based LASSO for a general single-index model

A general single-index model with high-dimensional predictors is considered.Additive structure of the unknown link function and the error is not assumed in this model.The consistency of predictor selection and estimation is investigated in this model.The index is formulated in the sufficient dimension reduction framework.A distribution-based LASSO estimation is then suggested.When the dimension of predictors can diverge at a polynomial rate of the sample size,the consistency holds under an irrepresentable condition and mild conditions on the predictors.The new method has no requirement,other than independence from the predictors,for the distribution of the error.This property results in robustness of the new method against outliers in the response variable.The conventional consistency of index estimation is provided after the dimension is brought down to a value smaller than the sample size.The importance of the irrepresentable condition for the consistency,and the robustness are examined by a simulation study and two real-data examples.

Empirical likelihood for single-index models with responses missing at random

The missing response problem in single-index models is studied, and a bias-correction method to infer the index coefficients is developed. Two weighted empirical log-likelihood ratios with asymptotic chisquare are derived, and the corresponding empirical likelihood confidence regions for the index coefficients are constructed. In addition, the estimators of the index coefficients and the link function are defined, and their asymptotic normalities are proved. A simulation study is conducted to compare the empirical likelihood and the normal approximation based method in terms of coverage probabilities and average lengths of confidence intervals. A real example illustrates our methods.

Tests for nonparametric parts on partially linear single index models

Tests for nonparametric parts on partially linear single index models are considered in this paper. Based on the estimates obtained by the local linear method, the generalized likelihood ratio tests for the models are established. Under the null hypotheses the normalized tests follow asymptotically the χ2-distribution with the scale constants and the degrees of freedom being independent of the nuisance parameters, which is called the Wilks phenomenon. A simulated example is used to evaluate the performances of the testing procedures empirically.

Prediction of Mechanical Properties of Hot Rolled Strip by Using Semi-Parametric Single-Index Model

A semi-parametric single-index model based approach was proposed for prediction of mechanical properties of hot rolled strip. Based on industrial production data, a semi-parametric single-index model was developed by choosing the appropriate kernel function and window width to predict the yield strength, tensile strength and elongation. When data samples are limited, compared with regression method and neural network method, the prediction results show that the semi-parametric single-index model based method is more adaptive and the prediction performance is superior to those by both regression and neural network methods.

Inference of change-point in single index models

Single index models are widely used in medicine, econometrics and some other fields. In this paper, we consider the inference of a change point problem in single index models. Based on density-weighted average derivative estimation (ADE) method, we propose a statistic to test whether a change point exists or not. The null distribution of the test statistic is obtained using a permutation technique. The permuted statistic is rigorously shown to have the same distribution in the limiting sense under both null and alternative hypotheses. After the null hypothesis of no change point is rejected, an ADE-based estimate of the change point is proposed under assumption that the change point is unique. A simulation study confirms the theoretical results.

Estimation of Daily Global Solar Radiation with Different Sunshine-Based Models for Some Burundian Stations

Sunshine duration (S) based empirical equations have been employed in this study to estimate the daily global solar radiation on a horizontal surface (G) for six meteorological stations in Burundi. Those equations include the Ångström-Prescott linear model and four amongst its derivatives, i.e. logarithmic, exponential, power and quadratic functions. Monthly mean values of daily global solar radiation and sunshine duration data for a period of 20 to 23 years, from the Geographical Institute of Burundi (IGEBU), have been used. For any of the six stations, ten single or double linear regressions have been developed from the above-said five functions, to relate in terms of monthly mean values, the daily clearness index () to each of the next two kinds of relative sunshine duration (RSD): and . In those ratios, G0, S0 and stand for the extraterrestrial daily solar radiation on a horizontal surface, the day length and the modified day length taking into account the natural site’s horizon, respectively. According to the calculated mean values of the clearness index and the RSD, each station experiences a high number of fairly clear (or partially cloudy) days. Estimated values of the dependent variable (y) in each developed linear regression, have been compared to measured values in terms of the coefficients of correlation (R) and of determination (R2), the mean bias error (MBE), the root mean square error (RMSE) and the t-statistics. Mean values of these statistical indicators have been used to rank, according to decreasing performance level, firstly the ten developed equations per station on account of the overall six stations, secondly the six stations on account of the overall ten equations. Nevertheless, the obtained values of those indicators lay in the next ranges for all the developed sixty equations:;;;, with . These results lead to assert that any of the sixty developed linear regressions (and thus equations in terms of and ), fits very adequately measured data, and should be used to estimate monthly average daily global solar radiation with sunshine duration for the relevant station. It is also found that using as RSD, is slightly more advantageous than using for estimating the monthly average daily clearness index, . Moreover, values of statistical indicators of this study match adequately data from other works on the same kinds of empirical equations.