Linear Regression Model
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MAUSAM ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 451-458
Author(s):  
TAPAN KANTI CHAKRABORTY

Lkkj & dksydkrk ¼vfyiqj½ ds U;wure rkieku dk okLrfod iwokZuqeku 12 ?kaVs iwoZ tkjh djus ds mn~ns’; ls ik¡p izkpyksa ij vk/kkfjr cgq jSf[kd lekJ;.k ekWMy fodflr fd;k x;k gS A blds iwoZ lwpdksa dk p;u vfyiqj os/k’kkyk ls izkIr lrg vk¡dM+ksa rFkk ekSle dk;kZy; dksydkrk ds fuEu Lrj ds iou vk¡dM+ksa ds vk/kkj ij fd;k x;k gS A ;g ekWMy 237 fnuksa ds ¼o"kZ 1997&2000 dh vof/k ds tuojh ,oa Qjojh ekg ds½ vk¡dM+ksa ds uewuksa rFkk dkQh yach vof/k ¼o"kZ 1988&2004½ ds U;wure rkieku ds vk¡dM+ksa es fLFkjrk dh tk¡p ds vk/kkj ij fodflr fd;k x;k gS A bl ekWMy dh tk¡p 178 fnuksa ds vk¡dM+ksa ds Lora= uewus ds vk/kkj ij dh xbZ gS A bl ekWMy dh {kerk dh tk¡p lkaf[;dh; vk¡dM+ksa ds vk/kkj ij dh xbZ gS vkSj bls ldkjkRed ik;k x;k gSA bl ekWMy dk mi;ksx ekSle iwokZuqekudRrkZ }kjk U;wure rkieku ds iwokZuqeku dk vkdyu djus ds fy, fd;k tk ldrk gS vkSj ;fn ckny rFkk iou dh xfr ds :[k esa ckn esa ifjorZu laHkkfor gks rks mlesa lq/kkj fd;k tk ldrk gS A  Five parameter multiple linear regression model for objective forecasting of minimum temperature of Kolkata (Alipore) with 12 hours lead period has been developed. The predictors are chosen from the available surface data of Alipore observatory and low level wind data of M. O. Kolkata. Model has been developed from data sample comprising of 237 days (in January and February, period: 1997 – 2000) after stationarity test of minimum temperature data of much longer period (1988–2004). The model is tested with independent sample of 178 days. Efficiencies of the model have been tested with statistical skill score and found to be positive. The model can be used by the forecaster for assessing prediction minimum temperature and modify if cloud cover and wind flow pattern are expected to change subsequently.  


Author(s):  
Nouhoum Bouare ◽  
Sebastien Bontems ◽  
Christiane Gerard

West Africa is reputed as an epicenter of HIV-2 infection. Studies undertaken in Mali suspected HIV-1 more prevalent. Our study aims to document HIV infectious profiles in Mali and analyze HIV-1 dominance. We documented HIV studies undertaken in Mali from1985 to 2010. We proceeded to a bibliographic search focused on theses from the Medicine Pharmacy Odontostomatology Faculty (FMPOS) of Bamako, survey reports, and abstracts or papers published in reviews with the reading committee. Documents were physically and virtually (via website) consulted and exploited. We gave preference to studies that discriminated against HIV serotypes. The data were analyzed according to study population/publication, representativeness, infectious profiles reporting, socio-demographic and clinical characteristics. HIV profiles variation in space and time was analyzed by using a linear regression model. Calculations were done using Excel software.


Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1540
Author(s):  
Zhengwu Cai ◽  
Chao Fan ◽  
Falin Chen ◽  
Xiaoma Li

The Landsat land surface temperature (LST) product is widely used to understand the impact of urbanization on surface temperature changes. However, directly comparing multi-temporal Landsat LST is challenging, as the observed LST might be strongly affected by climatic factors. This study validated the utility of the pseudo-invariant feature-based linear regression model (PIF-LRM) in normalizing multi-temporal Landsat LST to highlight the urbanization impact on temperature changes, based on five Landsat LST images during 2000–2018 in Changsha, China. Results showed that LST of PIFs between the reference and the target images was highly correlated, indicating high applicability of the PIF-LRM to relatively normalize LST. The PIF-LRM effectively removed the temporal variation of LST caused by climate factors and highlighted the impacts of urbanization caused land use and land cover changes. The PIF-LRM normalized LST showed stronger correlations with the time series of normalized difference of vegetation index (NDVI) than the observed LST and the LST normalized by the commonly used mean method (subtracting LST by the average, respectively for each image). The PIF-LRM uncovered the spatially heterogeneous responses of LST to urban expansion. For example, LST decreased in the urban center (the already developed regions) and increased in the urbanizing regions. PIF-LRM is highly recommended to normalize multi-temporal Landsat LST to understand the impact of urbanization on surface temperature changes from a temporal point of view.


PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258649
Author(s):  
Leander Melms ◽  
Evelyn Falk ◽  
Bernhard Schieffer ◽  
Andreas Jerrentrup ◽  
Uwe Wagner ◽  
...  

Pandemic scenarios like SARS-Cov-2 require rapid information aggregation. In the age of eHealth and data-driven medicine, publicly available symptom tracking tools offer efficient and scalable means of collecting and analyzing large amounts of data. As a result, information gains can be communicated to front-line providers. We have developed such an application in less than a month and reached more than 500 thousand users within 48 hours. The dataset contains information on basic epidemiological parameters, symptoms, risk factors and details on previous exposure to a COVID-19 patient. Exploratory Data Analysis revealed different symptoms reported by users with confirmed contacts vs. no confirmed contacts. The symptom combination of anosmia, cough and fatigue was the most important feature to differentiate the groups, while single symptoms such as anosmia, cough or fatigue alone were not sufficient. A linear regression model from the literature using the same symptom combination as features was applied on all data. Predictions matched the regional distribution of confirmed cases closely across Germany, while also indicating that the number of cases in northern federal states might be higher than officially reported. In conclusion, we report that symptom combinations anosmia, fatigue and cough are most likely to indicate an acute SARS-CoV-2 infection.


Author(s):  
Alemayehu Siffir Argawu ◽  
Gizachew Gobebo ◽  
Ketema Bedane ◽  
Temesgen Senbeto ◽  
Reta Lemessa ◽  
...  

The aims of this study was to predict COVID-19 new cases using multiple linear regression model based on May to June 2020 data in Ethiopia. The COVID-19 cases data was collected from the Ethiopia Ministry of Health Organization Facebook page. Pearson’s correlation analysis and linear regression model were used in the study. And, the COVID-19 new cases was positively correlated with the number of days, daily laboratory tests, new cases of males, new cases of females, new cases from Addis Ababa city, and new cases from foreign natives. In the multiple linear regression model, COVID-19 new cases was significantly predicted by the number of days at 5%, the number of daily laboratory tests at 10%, and the number of new cases from Addis Ababa city at 1% levels of significance. Then, the researchers recommended that Ethiopian Government, Ministry of Health, and Addis Ababa city administrative should give more awareness and protections for societies, and they should open again more COVID-19 laboratory testing centers. And, this study will help the government and doctors in preparing their plans for the next times.


Author(s):  
С.И. Носков

Разработаны две алгоритмические схемы оценивания параметров линейной регрессии с требованием равенства нулю ошибки аппроксимации для заданного наблюдения и на их основе способы расчета динамических оценок вкладов факторов, входящих в состав правой части линейной регрессионной модели, в значения зависимой переменной. Одна из этих схем основана на решении задачи квадратичного программирования, а вторая предусматривает использование взвешенного метода наименьших квадратов. Организованный при этом итерационный процесс предполагает пересчет матрицы весовых коэффициентов для каждого наблюдения обрабатываемой выборки данных. Рассчитаны вклады следующих факторов для регрессионной модели погрузки на железнодорожном транспорте: объема добычи угля, объема вывезенной древесины, рабочего парка груженых железнодорожных вагонов (в среднем в сутки). Установлено, что наибольшее влияние на выходную переменную оказывает объем добычи угля, хотя это влияние и имеет некоторую общую тенденцию к снижению: почти на 4 пункта за 14 лет. Также несколько ослабевает, на 3 пункта, влияние и второго по значимости фактора - рабочего парка груженых железнодорожных вагонов. А наименее значимый показатель (объем вывезенной древесины) имеет явную тенденцию к усилению своего влияния, которое выросло почти на 7 пунктов I developed two algorithmic schemes for estimating the parameters of linear regression with the requirement that the approximation error for a given observation is zero and, on their basis, methods for calculating the dynamic estimates of the contributions of the factors included in the right side of the linear regression model to the values of the dependent variable. One of these schemes is based on solving a quadratic programming problem, and the second involves the use of a weighted least squares method. The iterative process organized in this case involves recalculating the matrix of weighting coefficients for each observation of the processed data sample. I calculated the contributions of the following factors for the regression model of loading on railway transport: the volume of coal production, the volume of exported timber, the working fleet of loaded railway cars (on average per day). I found that the largest influence on the output variable is exerted by the volume of coal production, although this influence has some general tendency to decrease - by almost 4 points over 14 years. Also, the influence of the second most important factor - the working fleet of loaded railway cars, is also weakening by 3 points. But the least significant indicator - the volume of exported timber - has a clear tendency to increase its influence, which has grown by almost 7 points


Author(s):  
Mohammad Fayaz

Background: In the functional data analysis (FDA), the hybrid or mixed data are scalar and functional datasets. The semi-functional partial linear regression model (SFPLR) is one of the first semiparametric models for the scalar response with hybrid covariates. Various extensions of this model are explored and summarized. Methods: Two first research articles, including “semi-functional partial linear regression model”, and “Partial functional linear regression” have more than 300 citations in Google Scholar. Finally, only 106 articles remained according to the inclusion and exclusion criteria such as 1) including the published articles in the ISI journals and excluding 2) non-English and 3) preprints, slides, and conference papers. We use the PRISMA standard for systematic review. Results: The articles are categorized into the following main topics: estimation procedures, confidence regions, time series, and panel data, Bayesian, spatial, robust, testing, quantile regression, varying Coefficient Models, Variable Selection, Single-index model, Measurement error, Multiple Functions, Missing values, Rank Method and Others. There are different applications and datasets such as the Tecator dataset, air quality, electricity consumption, and Neuroimaging, among others. Conclusions: SFPLR is one of the most famous regression modeling methods for hybrid data that has a lot of extensions among other models.


2021 ◽  
Vol 14 (11) ◽  
pp. 7167-7185
Author(s):  
Fabian Weiler ◽  
Michael Rennie ◽  
Thomas Kanitz ◽  
Lars Isaksen ◽  
Elena Checa ◽  
...  

Abstract. The European Space Agency (ESA) Earth Explorer satellite Aeolus provides continuous profiles of the horizontal line-of-sight wind component globally from space. It was successfully launched in August 2018 with the goal to improve numerical weather prediction (NWP). Aeolus data have already been successfully assimilated into several NWP models and have already helped to significantly improve the quality of weather forecasts. To achieve this major milestone the identification and correction of several systematic error sources were necessary. One of them is related to small fluctuations of the temperatures across the 1.5 m diameter primary mirror of the telescope which cause varying wind biases along the orbit of up to 8 m s−1. This paper presents a detailed overview of the influence of the telescope temperature variations on the Aeolus wind products and describes the approach to correct for this systematic error source in the operational near-real-time (NRT) processing. It was shown that the telescope temperature variations along the orbit are due to changes in the top-of-atmosphere reflected shortwave and outgoing longwave radiation of the Earth and the related response of the telescope's thermal control system. To correct for this effect ECMWF model-equivalent winds are used as a reference to describe the wind bias in a multiple linear regression model as a function of various temperature sensors located on the primary telescope mirror. This correction scheme has been in operational use at ECMWF since April 2020 and is capable of reducing a large part of the telescope-induced wind bias. In cases where the influence of the temperature variations is particularly strong it was shown that the bias correction can improve the orbital bias variation by up to 53 %. Moreover, it was demonstrated that the approach of using ECMWF model-equivalent winds is justified by the fact that the global bias of model u-component winds with respect to radiosondes is smaller than 0.3 m s−1. Furthermore, this paper presents the alternative of using Aeolus ground return winds which serve as a zero-wind reference in the multiple linear regression model. The results show that the approach based on ground return winds only performs 10.8 % worse than the ECMWF model-based approach and thus has a good potential for future applications for upcoming reprocessing campaigns or even in the NRT processing of Aeolus wind products.


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