Search

In this study, Mann–Kendall (MK), Spearman's rho (SR), and innovative trend analysis with significance test (ITA-ST) are performed on about 53 years of meteorological parameters obtained from 23 meteorological stations located in the lower Tigris–Euphrates basin (LTEB), Türkiye. Finally, sequential Mann–Kendall (SMK) and Cusum tests are applied to detect any abrupt changes in annual time series. Results indicate that MK and SR demonstrate a significant trend in seven of the total annual precipitation series, and ITA-ST captures the existence of a significant trend in 21 of the 23 total annual precipitations. Three methods reveal that there is an increasing trend in both the annual mean temperature and the annual total evapotranspiration (EP). MK, SR, and ITA-ST capture a significant decreasing trend in the 10, 8, and 16 of the 23 annual mean relative humidity (RH) series, respectively. According to the findings, ITA-ST is more sensitive than the classical MK and SR methods. Cusum and SMK tests are detected the start of trend year 21.7 and 8.6% of annual total precipitation, 95.65 and 69.56% of annual mean temperature, 47.82 and 17.4% of total mean RH, and 95.65 and 69.56% of annual total EP time series, respectively. The Cusum test is found to be more sensitive than the SMK test. HIGHLIGHTS The MK, SR, and ITA-ST tests were first used for the relative humidity.; Three different methodologies show that both the temperature and the evapotranspiration are on the rise.; The ITA-ST technique is more sensitive than conventional MK and SR techniques.; MK, SR, and ITA-ST display a significant decreasing trend in the annual mean relative humidity series.;

The medical reform carried out in Ukraine in recent years has led to the emergence of a number of managerial problems faced by the heads of health care institutions. Now they are forced to perform the entire scope of management functions, including the assessment and analysis of performance of the headed institutions. The article poses the problem of inconsistency of the arrays of primary data for assessing and analyzing the efficiency of health care institutions and secondary economic and statistical models of processing these data in order to useful information support regarding the actions of the management of institutions. The structure and content of primary data do not fit any model of further processing due to the non-compliance of the system for collecting arrays of primary data with the key requirements and principles of forming such arrays. The purpose of the article is to identify, on the basis of the calculation experiment, the key problems of domestic approaches to the formation of primary data for the use of non-parametric models for assessing the performance of health care institutions and to develop recommendations for solving these problems. To solve the tasks set, the world experience on the effective application of non-parametric models for evaluating the effectiveness of health care institutions was analyzed, the appropriate factual material was selected and a calculation experiment was carried out to establish a simple causal relationship between the indicators of evaluation of health care institutions, which are components of statistical accounting indicators approved by the State in this sphere. It is found that currently a very large number of indicators are used for evaluation, with excessive use of cost effectiveness indicators, while the statistical relationship of the behavior of indicators against each other is insignificant or absent altogether because the dominant principle of the selection of indicators is statistical availability and regularity of initial data. The main directions of solving the indicated problem are: changing approaches to the formation of primary indicators for the activities of health care institutions in general, necessarily basing not on the simplicity of their calculation, but on the requirements of models of further economic and statistical processing; structuring of the primary information base according to the directions of institutional interaction at the local level and uniform filling with appropriate and adequate assessment indicators by each of the directions.

This article is devoted to methods of processing random processes. This task becomes particularly relevant in cases where the random process is broadband and non-stationary; then, the measurement of a random process can be associated with an assessment of its probabilistic characteristics. Very often, a non-stationary broadband random process is represented by a single implementation with a priori uncertainty about the type of distribution function. Such random processes occur in information and measuring communication systems in which information is transmitted at a real-time pace (for example, radio telemetry systems in spacecraft). The use of methods of traditional mathematical statistics, for example, maximum likelihood methods, to determine probability characteristics in this case is not possible. In addition, the on-board computing systems of spacecraft operate under conditions of restrictions on mass-dimensional characteristics and energy consumption. Therefore, there is a need to apply accelerated methods of processing measured random processes. This article discusses a method of processing non-stationary broadband random processes based on the use of non-parametric methods of decision theory. An algorithm for dividing the observation interval into stationary intervals using non-parametric Kendall’s statistics is considered, as are methods for estimating probabilistic characteristics on the stationary interval using ordinal statistics. This article presents the results of statistical modeling using the Mathcad program.

In the present study, five parametric and non-parametric methods: linear regression (LR), conventional Mann–Kendall (MK), modified Mann–Kendall (MMK), Spearman's Rho (SR) and Innovative Trend Method (ITM) were used to identify trends in the groundwater levels of 60 piezometers distributed uniformly across Sirjan plain, Iran, from 2005 to 2018. The LR method was found to be affected by the presence of outliers and autocorrelation. The conventional non-parametric tests (MK and SR) were not able to offset the effects of the autocorrelations between the groundwater level data. The ITM method was also found to be a not so comprehensive and precise statistical tool for trend analysis because it does not provide a quantitative index for identifying trend significance. Therefore, the MMK test was found to be the most appropriate trend analysis method among the five trend identification methods used in this study by eliminating the effect of all significant autocorrelation coefficients. The results of the MMK test showed that the groundwater levels in Sirjan plain had witnessed significant decreasing trends during the study period. In only 24 months (out of a total 10,080 studied months), no significant decreasing trends in groundwater levels were observed. HIGHLIGHTS Five various parametric and non-parametric methods were used to analyze the trend of groundwater level.; The effect of outliers and autocorrelation in time series was investigated.; The results indicated that the Mann-Kendall test after elimination of the effect of all significant autocorrelation had the best performance.; The effects of human activities on management of critical situation in Sirjan Plain, Iran were investigated.;

In this study, 9 non-parametric stability methods were used to evaluate the environment interaction on grain yield of 8 triticale varieties. The genotypes were evaluated for grain yield in the region of Sadovo for 5 years. A combined analysis of variance, non-parametric stability statistics and rank correlations among them were determined. Significant differences were detected between genotypes and their EGIs. Different non-parametric stability statistics were used to determine stability of the studied genotypes. The level of association among the statistics was assessed using Spearman's rank correlation. Rank-correlation coefficients between yield and some non-parametric stability statistics were highly significant. Genotypes mean yield was significantly correlated to the non-parametric stability statistics NP(4) (r = 0.833*), NP(3) (r = 0.762*) and S(3) (r = 0.738*). In conclusion, based on most non-parametric stability statistics, the genotypes Indiana and Orbital were found to be the most stable and high yielding. These genotypes can be used for the improvement of adaptation and high yielding in triticale breeding programs. [ABSTRACT FROM AUTHOR]