In this article, the tests on parallelism, equal intercept and sets of lines in-tersected at a fixed value for a set of r simple Linear Models or a set of r Linearizable regression Models are generalized to the Multivariate case, r = 2; 3; : : :; R. Likewise, the normality hypothesis is replaced assuming an elliptical matrix variate distribution, concluding that the tests obtained under normality are valid and are invariant under the whole family of elliptical matrix variate distributions. Finally, an application in an agricultural acarology context is provided.

Infiltration is the most important soil physical characteristic which its direct measurement is laborious, time consuming and expensive. The purpose of this study is to estimate steady infilterability rate, using Neuro-Fuzzy, Neural Network and Multivariate Linear Regression Models. Consequently, steady infilterability rate, was measured using double rings in 100 points in Dehgolan region, Kurdistan Province, Iran. Soil physical (porosity, bulk density, sand, silt and clay) and topography characteristics were measured as readily available properties and used to estimate steady infilterability rate, The data were divided into two sets of terrain (70% of the data) and test (30% of the data). The Models based on input type were categorized into type 1 (physical characteristics) and 2 (soil physical and topography characteristics). The results based on mean bias error (MBE), Root Mean Square Error (RMSE), Mean Error (ME), Relative Standard Error (RSE) and Relative Improvement (RI) showed that the Neuro-Fuzzy model (type 1 respectively with statistics 0. 24, 2. 01, 0. 46, 4. 04 and 46. 65) (type 2 respectively with statistics-0. 1, 1. 24, 0. 23, 1. 54 and 58. 62) has the most accuracy of steady infilterability rate, estimation. Also was observed using topography data as input together with soil physical characteristics can lead to improvement of the estimation accuracy of steady infilterability rate.

This study aims to compare the ability of dynamic artificial neural network (DANN) and Multivariate Linear regression (LR) in forecasting monthly inflow to Shahcheraghi reservoir in Semnan province, Iran. The input data consisted monthly flow discharge, precipitation, mean temperature and snow cover area. Snow cover area was estimated using NOAA-AVHRR images, based on thresholds in histograms of different phenomena in visible and thermal channels. Dynamic artificial neural networks were determined with one hidden layer, Levenberg-Marquardt as training function, and sigmoid as transfer function Moreover, five DANN and five LR Models were run with different input data and the results were compared. Root mean square (RMSE), mean bias error (MBE), mean absolute relative error (MARE), maximum relative error (REmax) and R2 (coefficient of determination) are the criteria that were used for Models evaluation. The best result is gained with three inputs (inflow discharge, precipitation and snow cover area) by DANN. Regarding Linear regression as a classic model in inflow forecasting, the improvement of the results by using DANN was obvious. The REmax of the selected DANN model was almost 85% less than REmax of the selected LR.

BACKGROUND AND OBJECTIVES: The classification of marine animals as protected species makes data and information on them to be very important. Therefore, this led to the need to retrieve and understand the data on the event counts for stranded marine animals based on location emergence, number of individuals, behavior, and threats to their presence. Whales are generally often stranded in very shallow areas with sloping sea floors and sand. Data were collected in this study on the incidence of stranded marine animals in 20 provinces of Indonesia from 2015 to 2019 with the focus on animals such as Balaenopteridae, Delphinidae, Lamnidae, Physeteridae, and Rhincodontidae. METHODS: Multivariate latent generalized Linear model was used to compare several distributions to analyze the diversity of event counts. Two optimization Models including Laplace and Variational approximations were also applied. FINDINGS: The best theta parameter in the latent Multivariate latent generalized Linear latent variable model was found in the Akaike Information Criterion, Akaike Information Criterion Corrected and Bayesian Information Criterion values, and the information obtained was used to create a spatial cluster. Moreover, there was a comprehensive discussion on ocean-atmosphere interaction and the reasons the animals were stranded. CONCLUSION: The changes in marine ecosystems due to climate change, pollution, overexploitation, changes in sea use, and the existence of invasive alien species deserve serious attention.

Let V and W be two real vector spaces and let ~ be a relation on both V and W. A Linear function T : V ® W is said to be a Linear preserver (respectively strong Linear preserver) of ~ if Tx ~ Ty whenever x ~ y (respectively Tx ~ Ty if and only if x ~ y). In this paper we characterize all Linear functions T : Mn,m ® Mn, k which preserve or strongly preserve Multivariate and directional majorization.

Biodiversity is an important structural feature of dynamic and complex forest ecosystems. One of the most challenging and important issues in assessing the structure of forest ecosystems is understanding the relationship between biodiversity and environmental factors. Hyrcanian Forests are considered a biodiversity hotspot in the world and have special and unique features that have led to an emphasis and importance of biodiversity conservation in these forests. The aim of this study was to investigate the effect of biotic and abiotic factors on the diversity and richness of tree species in Hyrcanian Forests from the west of Gilan province to the east of Golestan province. For this purpose, using 655 fixed sample plots (0. 1 hectare), the diversity of trees in 3 provinces in the northern Iran from east to west of the Caspian Sea was analyzed. A combination of non-parametric Models including random forest (RF) and support vector machine (SVM) and Linear regression Models were used to investigate the relationship between tree diversity and biotic and abiotic factors. Biotic and abiotic variables included the number of trees per hectare, diameter, respectively. Basal area (BA), Basal Area in Largest tree (BAL), slope, aspect and elevation. Evaluation statistics including the coefficient of determination, RMSE and percentage RMSE error showed that the random forest model was the best model to determine the relationship between biodiversity and environmental factors and has suitable accuracy for determining biodiversity changes in the northern forests of Iran.

In the last few decades, profile monitoring in univariate and Multivariate environment has drawn a considerable attention in the area of statistical process control. In Multivariate profile monitoring, it is required to relate more than one response variable to one or more explanatory variables. In this paper, the Multivariate multiple Linear profile monitoring problem is addressed under the assumption of existing autocorrelation among observations. Multivariate Linear mixed model (MLMM) is proposed to account for the autocorrelation between profiles. Then two control charts in addition to a combined method are applied to monitor the profiles in phase II. Finally, the performance of the presented method is assessed in terms of average run length (ARL). The simulation results demonstrate that the proposed control charts have appropriate performance in signaling out-of-control conditions.