The problem in this study is how the influence of the Independent Board of Commissioners, Audit Committee, Leverage and Capital Intensity on manufacturing companies listed on the Indonesia Stock Exchange in the year. The purpose of this study was to examine the influence of the Independent Board of Commissioners, Audit Committee, Leverage and Capital Intensity on manufacturing companies listed on the Indonesia Stock Exchange. The population of this research is all manufacturing companies listed on the Indonesia Stock Exchange. The sampling method used purposive sampling and obtained a sample of 116 companies. The analysis technique used is simple linear regression analysis with the help of SPSS. The proxy used to measure Tax Avoidance uses the Tax Expense divided by Pre-Tax Profit. The proxy used to measure the Independent Board of Commissioners using Independent Commissioners is divided by the total number of members. The proxy used to measure the Audit Committee uses Dummy which is worth 1 if the audit committee is> 3, and is worth 0 if the audit committee <3. The proxy used to measure leverage uses Total Debt divided by Total Assets. For the proxies used to measure Capital Intensity using Total Net Fixed Assets divided by Total Assets. The results of hypothesis testing obtained with a significant level of 5% indicate that only the audit committee has an influence with a significance value of 0.029 <0.05 against tax avoidance. While the results of hypothesis testing for independent board of commissioners, leverage and capital intensity have no effect on tax avoidance.
Air transportation is the most appropriate option for extremely vast distances, such as those between cities, provinces, and countries. While unpredictability, high volatility, and seasonality sometimes result in complex behavior in air passenger time series, this research applies the Singular Spectrum Analysis technique for air passengers data and uses the linear recurrent type for forecasting. Trends, seasonality, cyclists, and noise can all be found and extracted using Singular Spectrum Analysis. Singular Spectrum Analysis has the potential to be a highly effective forecasting method.
Indonesia is known for its excessive rainfall. Rainfall trends in an area have different characteristics. Differences in latitude, apparent motion of the sun, geographical position, topography, and the interaction of many forms of air circulation all contribute to this. Rainfall time series is essential for engineering planning, particularly for water infrastructure like irrigation, dams, urban drainage, ports, and wharves. Although meteorological technologies provide short-term rainfall predictions, long-term rainfall prediction is difficult and fraught with uncertainty. Unpredictability and seasonality can cause complex behavior in rainfall time series. This research utilizes the Singular Spectrum Analysis approach to extract trends; seasonality, cyclists, and noise can all be identified with potentially high accuracy.
This paper discusses the minimax estimator of parameter for binomial distribution. The likelihood function is constructed based on the probability function of the Binomial distribution. The posterior distribution is obtained from the joint of the likelihood function and prior distribution. Furthermore, the Bayes estimator is obtained based on the posterior mean and provide the constancy of the risk of Bayes the minimax estimator can be concluded.
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