Over-whitening or Pre-whitening in Hydro-climatological Trend Analysis?

To meet the basic assumption of classical Mann-Kendall (MK) trend analysis, which requires serially independent time series, a pre-whitening (PW) procedure is proposed to alleviate the serial correlation structure of a given hydro-meteorological time series records for application. The procedure is simply to take the lagged differences in a given time series in the hope that the new time series will have an independent serial correlation coefficient. The whole idea was originally based on the first-order autoregressive AR (1) process, but such a procedure has been documented to damage the trend component in the original time series. On the other hand, the over-whitening procedure (OW) proposes a white noise process superposition of the same length with zero mean and some standard deviation on the original time series to convert it into serially independent series without any damage to the trend component. The stationary white noise addition does not have any trend components. For trend identification, annual average temperature records in New Jersey and Istanbul are presented to show the difference between PW and OW procedures. It turned out that the OW procedure was superior to the PW procedure, which did not cause a loss in the original trend component.

The Influence of English Teachers’ Competency on Students’ Learning Achievement

This correlation study aimed to investigate a correlation between English teacher competency and students’ learning achievement at State Junior High School 1 Tempuling, Indragiri Hilir Regency, Riau. There was 1 English teacher who has certified as a professional teacher. Seventy-five students taught by the English teacher became the sample through random sampling technique; meanwhile, the English Teacher Performance Assessment documentation and evidence of students’ learning achievement in their report card were used to collect the data. The data were analyzed using serial correlation. The finding shows that the correlation (rxy) score = 0.438 compared to rt. Because rxy was higher at 5% and 1% significance levels, it was possible to conclude a significant correlation between English teacher competency and students’ learning achievement.

Record tests to detect non-stationarity in the tails with an application to climate change

The analysis of trends and other non-stationary behaviours at the extremes of a series is an important problem in global warming. This work proposes and compares several statistical tools to analyse that behaviour, using the properties of the occurrence of records in i.i.d. series. The main difficulty of this problem is the scarcity of information in the tails, so it is important to obtain all the possible evidence from the available data. First, different statistics based on upper records are proposed, and the most powerful is selected. Then, using that statistic, several approaches to join the information of four types of records, upper and lower records of forward and backward series, are suggested. It is found that these joint tests are clearly more powerful. The suggested tests are specifically useful in analysing the effect of global warming in the extremes, for example, of daily temperature. They have a high power to detect weak trends and can be widely applied since they are non-parametric. The proposed statistics join the information of M independent series, which is useful given the necessary split of the series to arrange the data. This arrangement solves the usual problems of climate series (seasonality and serial correlation) and provides more series to find evidence. These tools are used to analyse the effect of global warming on the extremes of daily temperature in Madrid.

Poverty, Income Inequality and Economic Growth in Nigeria (1981-2019)

This study empirically examined the relationship between poverty, income inequality and economic growth in Nigeria. The study used time series data from National Bureau of Statistics (NBS) and Central Bank of Nigeria (CBN) Statistical Bulletin between the periods from 1981 to 2019. The study employed the use of Augmented Dickey Fuller test, Co integration test and Error Correction technique. The unit root test results indicated that all the variables were stationary at first difference and co-integration test confirmed a long run relationship among the variables. The error correction model shows that about 96 percent of the discrepancy between the actual and the equilibrium value of economic growth is corrected or eliminated each year. The coefficient of determination (R2) is 0.68 which shows that about 68 percent variations in the economic growth were explained by the independent variables . Furthermore, the Breusch-Godfrey Serial Correlation LM Test shows that the probability of the chi-square (2) is 0.2775 and this is greater than 0.05 at 5% significance level. This therefore confirms the absence of serial correlation. Also, the Breusch-Pagan-Godfrey Heteroscadaticity test indicates that the probability of chi-square (5) is 0.1242 and this is greater than 0.05 at 5% significant level. This also confirms the absence of heteroscedasticity in the model. From the study, the findings revealed that income inequality has a negative relationship with economic growth in the country while poverty was found to be positively related to economic growth. Similarly, the findings also revealed that poverty and income inequality has an insignificant effect on economic growth in Nigeria. Based on the findings, it can be concluded that poverty and income inequality has not significant relationship with economic growth in Nigeria. Thus, the study concludes that there is need for government of the country to come up with an all-inclusive policy and programme that will be targeted to the poor and give them ample opportunities to improve their welfare.

Quantifying the rarity of extreme multi-decadal trends: how unusual was the late twentieth century trend in the North Atlantic Oscillation?

Climate trends over multiple decades are important drivers of regional climate change that need to be considered for climate resilience. Of particular importance are extreme trends that society may not be expecting and is not well adapted to. This study investigates approaches to assess the likelihood of maximum moving window trends in historical records of climate indices by making use of simulations from climate models and stochastic time series models with short- and long-range dependence. These approaches are applied to assess the unusualness of the large positive trend that occurred in the North Atlantic Oscillation (NAO) index between the 1960s to 1990s. By considering stochastic models, we show that the chance of extreme trends is determined by the variance of the trend process, which generally increases when there is more serial correlation in the index series. We find that the Coupled Model Intercomparison Project (CMIP5 + 6) historical simulations have very rarely (around 1 in 200 chance) simulated maximum trends greater than the observed maximum. Consistent with this, the NAO indices simulated by CMIP models were found to resemble white noise, with almost no serial correlation, in contrast to the observed NAO which exhibits year-to-year correlation. Stochastic model best fits to the observed NAO suggest an unlikely chance (around 1 in 20) for there to be maximum 31-year NAO trends as large as the maximum observed since 1860. This suggests that current climate models do not fully represent important aspects of the mechanism for low frequency variability of the NAO.

Efficient Change-Points Detection For Genomic Sequences Via Cumulative Segmented Regression

Motivation Knowing the number and the exact locations of multiple change points in genomic sequences serves several biological needs. The cumulative segmented algorithm (cumSeg) has been recently proposed as a computationally efficient approach for multiple change-points detection, which is based on a simple transformation of data and provides results quite robust to model mis-specifications. However, the errors are also accumulated in the transformed model so that heteroscedasticity and serial correlation will show up, and thus the variations of the estimated change points will be quite different, while the locations of the change points should be of the same importance in the original genomic sequences. Results In this study, we develop two new change-points detection procedures in the framework of cumulative segmented regression. Simulations reveal that the proposed methods not only improve the efficiency of each change point estimator substantially but also provide the estimators with similar variations for all the change points. By applying these proposed algorithms to Coriel and SNP genotyping data, we illustrate their performance on detecting copy number variations. Supplementary information The proposed algorithms are implemented in R program and are available at Bioinformatics online.

THE RESPONSE OF WHEAT PRODUCTIVITY TO CLIMATE PATTERNS: EVIDENCE FROM PAKISTANI DISTRICTS

The study examines the impact of climate variables on wheat production in 10 major wheat-producing districts of Pakistan. In doing so, we apply the Driscoll–Kraay approach to estimate the panel data from 1981 to 2019. Our empirical analysis reveals that climate variables, including temperature, rainfall and humidity, follow a common correlation across districts. We find that wheat productivity and temperature, as well as rainfall, follow an inverted U-shaped relation. The response of the wheat productivity is quadratic rather than linear towards average temperature and rainfall during the specific time of cultivation, including planting, flowering and harvesting. Besides, fertilizer use promotes and humidity impedes wheat productivity. Our findings are robust considering heterogeneity, serial correlation and spatial dependency.