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The Value at Risk (VaR) method refers to a statistical risk measurement tool used to determine the maximum loss of an investment, while the distribution that must be met is the normal distribution. This is not in line with the actual situation, because the distribution of the return value is found to be not normally distributed but depends on market conditions that occurred at that time, thus invalidating the VaR estimate and resulting in greater portfolio risk. Therefore, in this study, the estimation of risk value will be carried out using the Gumbel Copula method which can model the dependency structure between stocks and is flexible enough to model financial return data from https://finance.yahoo.com/. The parameter estimates produced by the Gumbel Copula method are then used to calculate the VaR at 90%, and 99% confidence levels. The resulting VaR values ​​are 0,076 and 0.231. To test the feasibility of the VaR model, backtesting was carried out and concluded that the VaR value obtained was valid and suitable for use in the risk assessment of PT. XL Axiata Tbk and PT. Telkomunikasi Indonesia Tbk.

Menguji efek mediasi dari profitabilitas pada efek krisis pandemi COVID-19 terhadap kebijakan dividen di Indonesia merupakan tujuan penelitian ini. Perusahaan sektor real estat, properti, dan konstruksi bangunan dipilih sebagai sampel dengan pendekatan purposive sampling technique, dengan kriteria-kriteria yang ditetapkan, serta periode penelitian ini adalah tahun 2014 – 2020. Alat analisis statistik yang digunakan adalah model persamaan struktural berbasis kovarian untuk pengujian kausalitas dengan melakukan uji robustness pada dua pendekatan model empiris. Hasil yang dihasilkan adalah krisis pandemi COVID-19 berpengaruh positif terhadap profitabilitas secara positif, dan profitabitas berpengaruh positif terhadap kebijakan dividen. Pada uji mediasi, terbukti krisis pandemi COVID-19 berpengaruh positif terhadap kebijakan dividen yang secara signifikan yang dimediasi oleh profitabilitas. Hasil penelitian ini menunjukkan bahwa perusahaan sektor real estat, properti, dan konstruksi bangunan di Indonesia cenderung menekan tingkat dividen selama krisis pandemi. Bagi manajemen perusahaan agar dapat memperhatikan efek mediasi profitabilitas perusahaan dalam menetapkan kebijakan dividen secara optimal di masa krisis. Bagi para investor, dapat memperhatikan efek mediasi profitabilitas perusahaan untuk mendapatkan return yang optimal berupa dividen, secar spesifik untuk perusahaan sektor riil estat, properti dan konstruksi bangunan selama pandemi.

Stock is one of the popular financial market instruments. Issuing shares are one of the company's choices when deciding to fund a company. The uncertainty of stock prices in the stock market is an important event to be taken into consideration in making a decision by investors so that a model is needed to describe a stock event. GARCH Dynamic Conditional Correlation (DCC) is a model with a conditional and variance time-dependent that describes the dynamics of stock volatility. This study discusses the DCC GARCH model equation which is applied to the LQ 45 data. The model obtained for BCA shares 𝑸t = + + so it can be concluded that DCC GARCH is more appropriate for BCA shares.

The deadliest infectious disease in Indonesia is tuberculosis (TB), and South Sulawesi is one of the provinces that contributed the most tuberculosis cases in Indonesia in 2018 with 84 cases per 100,000 population. This study aims to identify variables that could explain the proportion of TB cases in South Sulawesi. The data used has many explanatory variables, and there are outliers. Sparse Least Trimmed Squares (LTS) analysis can be used to handle data that has many explanatory variables and outliers. The resulting sparse LTS model successfully selects and shrinks the variables to 14 variables only. In addition, based on the value of R2 and RMSE for the model evaluation, the sparse LTS shows satisfying results rather than classical LASSO. The government can focus on these factors if they want to reduce the proportion of TB cases in South Sulawesi., Penyakit menular paling mematikan di Indonesia adalah tuberkulosis (TB), dan Sulawesi Selatan merupakan salah satu provinsi penyumbang kasus tuberkulosis terbanyak di Indonesia pada tahun 2018 dengan 84 kasus per 100.000 penduduk. Penelitian ini bertujuan untuk mengidentifikasi variabel yang dapat menjelaskan proporsi kasus TB di Sulawesi Selatan. Data yang digunakan memiliki banyak variabel penjelas, dan terdapat outlier. Analisis Sparse Least Trimmed Squares (LTS) dapat digunakan untuk menangani data yang memiliki banyak variabel penjelas dan pencilan. Model sparse LTS yang dihasilkan berhasil menyeleksi dan menyusutkan variabel menjadi hanya 14 variabel. Selain itu, berdasarkan nilai R2 and RMSE untuk evaluasi model, sparse LTS menunjukkan hasil yang memuaskan daripada LASSO klasik. Faktor-faktor tersebut bisa menjadi fokus pemerintah jika ingin menurunkan proporsi kasus TB di Sulawesi Selatan.

This study examined factors that are assumed to influence the profitability of maritime companies as determinants of company performance in relation to the development of marine human resource development as stipulated in PP 47 of 2012 and PP 51 of 2012. Hypothesis testing was carried out to test the direction and significance of influence of formulated hypotheses The results showed that company age positively affect the profitability of maritime companies. Other factors that had been assumed to affect company profitability turned out to show no significant influences including company size, company growth, and capital structure. Maritime companies are recommended to take into account the managerial implications of this study, while the government is expected consider company size in formulating any regulation regarding PP 51 of 2012.

Disaster cases need to be analyzed considering that when a disaster occurs it will have an extraordinary impact. The statistical method that can be used to study disaster cases is a simple correspondence analysis. This study aims to map areas with the potential for natural disasters in the province of Central Sulawesi. So, in the analysis, regions are grouped according to row profile values that are greater than the average. The result of simple correspondence analysis obtained flood disaster has the potential to occur in Banggai, Morowali, Donggala, Buol, Parigi Moutong, Tojo Una-una, Sigi, and North Morowali. While the dominant tornado disaster occurred in Banggai Kepulauan, Banggai, Poso, Toli-toli, Parigi Moutong and Sigi. For regional landslides with potential Banggai Islands, Donggala, Toli-toli, Parigi Moutong, and Sigi. Then Banggai Islands and the City of Palu are the dominant regions for earthquake disasters. The results of the grouping can be the basis of government and community focus in tackling the dominant disasters occurring in their respective regions so as to minimize the impact when natural disasters occur.

The aim of this study was to identify and classify the student’s concept image and its influence on the reasoning of the problem-solving of the derivative. The research used a qualitative description approach and used eight research subjects. From the answers collected upon the given problems, we obtained several variations of students’ concept images, thus it showed how students’ concept image influenced the reasoning. In order to clarify and classify the characteristic of the obtained answers, we summarized there were three categories of the concept image of the derivative, namely symbolically related to a basic formula of the derivative of a function, limit of the ratio of difference value of the functions, and the properties of the derivative of the functions. Furthermore, our study suggested that each student’s concept image affecting the reasoning of the derivative. In addition, we found some misperceptions in answering the problem and misconception in the use of the basic formula of the derivative of the functions among the students’ answers.

This research investigates the impact of crisis due to the COVID-19 pandemic on the dividend policy of green index companies in Indonesia, namely the Sustainable and Responsible Investment (SRI) by Biodiversity (KEHATI) Foundation, or SRI-KEHATI indexed companies. The purposive sampling technique was used to collect data from companies listed from 2014 to 2020, using static and dynamic panel data models. From the several panel data models tested, the static panel data regression with random effects model (REM) and fixed effect model (FEM) uses the least square dummy variable-robust standard error (LSDV-RSE) technique are the best econometric models feasible. The system generalized method of moments (SYS-GMM) is used as a suitable econometric model with a robustness test used to determine static panel data regression. It is reported that SRI-KEHATI indexed companies tend to distribute dividends positively during this crisis, and is also statistically proven robust. This gives a positive signal to the capital market concerning the sluggish trading activity. The market reaction test, using two-approaches, showed that this business did not provide a positive reaction to the capital market, which turned out to be pessimistic. Furthermore, profitability and financial leverage have a robust effect, while dividends from the previous year affect dividend policy on the static panel data model, and firm size affect dividend policy on SYS-GMM. Predictors that proved influential with a direction not in line with the hypothesis were investment opportunities on REM and SYS-GMM, and firm age on SYS-GMM. The parameter estimation that passes the model specification test is feasible, whiles the biased and inconsistency of parameter estimation due to the alleged correlation between ui,t and PYDi,t failed to occur in static panel data regression. The endogeneity issue was resolved by dynamic panel data regression with the strongest corrective effect. This research can be used as a reference for investors to obtain optimal returns on green index companies in the country. An optimal dividend policy can increase the value of the SRI-KEHATI indexed companies; therefore, it can contribute optimally to sustainability and responsibility for social and environmental aspects.

A widely used estimation method in estimating regression model parameters is the ordinary least square (OLS) that minimizes the sum of the error squares. In addition to the ease of computing, OLS is a good unbiased estimator as long as the error component assumption () in the given model is met. However, in the application, it is often encountered violations of assumptions. One of the violation types is the violation of distributed error assumption which is caused by the existence of the outlier on observation data. Thus, a solid method is required to overcome the existence of outlier, that is Robust Regression. One of the Robust Regression methods commonly used is robust MM method. Robust MM method is a combination of breakdown point and high efficiency. Results obtained based on simulated data generated using SAS software 9.2, shows that the use of objective weighting function tukey bisquare is able to overcome the existence of extreme outlier. Furthermore, it is determined that the value of tuning constant c with Robust MM method is 4.685 and it is obtained95% of efficiency. Thus, the obtained breakdown point is 50%.

The importance of learning mathematics can not be separated from its role in all aspects of life. This research aims to analyze the achievement of the students’ self-proficiency who are taught by using cooperative learning with Team Assisted Individualization (TAI) and conventional learning. Students need to possess self-proficiency ability well so that they could have confidence that they are capable of confronting and of solving their daily life problems in general or mathematical tasks in particular. The population in this research was students of Statistics study program at one of public universities in Makassar. The sampling technique used in this research was purposive sampling, while the instrument used was self-proficiency scale (SPr) which has been validated. The data were analyzed by using parametric and non-parametric statistics. The result of this research is that the achievement of the students’ self-proficiency who are taught by using cooperative learning with TAI is better than students who are taught by using conventional learning.

Regresi logistik multinomial merupakan perluasan dari regresi logistik biner yang memungkinkan lebih dari dua kategori variabel dependen. Pada paper ini akan membahas penaksiran parameter regresi logistik multinomial melalui Generalized Method of Moment (GMM). Generalized Method of Moment (GMM) merupakan salah satu metode yang dapat mengatasi pelanggaran asumsi pada data seperti autokorelasi dan heteroskedastisitas

Jackknife is re-sampling bias estimation method, in predict standard deviations. The principle of the Jackknife method in regression parameters estimation is to eliminate one data and repeat it as much as the number of existing data samples. The step in estimating regression parameter of Jackknife Ridge (JR) was the first to transform data through cantering and scaling process and orthogonalization on independent variables. The second determines initial value k0 and perform the iteration process. The third by transform initial estimator JR and finally by testing the feasibility of the resulting model of Jackknife ridge regression. Results obtained, iteration process is conducted until obtained a value of | ( α ˆ ′ GR α ˆ GR ) i − ( α ˆ ′ GR α ˆ GR ) i-1 | ⩽ 0 , 0001 and iteration stops at 5th iteration, so obtain estimator value of generalized ridge coefficient of orthogonal independent variable ( α ˆ _GR ) value of generalized ridge coefficient of orthogonal independent variable ( α ˆ GR ) .

There are various variance analysis tests, one of which is the Friedman test, it is used as an alternative to two way variance analysis technique. If there are unmet assumptions in the Friedman test, that is, they are not normally distributed and do not have a homogeneous variant, then the multivariate Page test can be used to determine whether there is a median difference between k (k ⩾ 3), a median population. The Page test will be applied in the case of the growth rate of soybean plants that obtain 5 types of treatment that are conditioned, namely seedling, watering, giving 100% KL, 75%, and 50% KL Based on statistical test using multivariate page indicated that there is influence on 5 types treatment of seed conditioning i.e dry seed (control), distilled water, 100 g PEG L-1 of water, 200 g PEG L-1 of water, and 300 g PEG L-1 of water against 3 response measurements i.e germination, growth rate and growing Randomness Of Soybean.

Stock assessment in any fisheries scientific study is an effort to determine the productivity of fishery resource, the effect of catch on resources and changes in catch patterns, for example due to implementation of management and development policies. It involves 3 (three) main aspects: the first, determine total biomass by using surplus production model; the second, determine recruitment (the number of fish that has reached the age during one recruitment season) can use Ricker model; the third, determine the equation of growth in the fishery can use Von Bertalanffy model. Mathematical models are mostly used to describe the dynamics of fish populations. In the use of appropriate models in stock assessment will result in a more appropriate basis in selecting fishing methods and advanced analysis of catches, hence required a plan in the sustainable management and utilization of fish resources. The study of sustainable dynamic of lemuru fisheries management in Bali strait is the dynamics of biomass of lemuru fishery by using the biological parameters yield of some surplus production models. By obtain the biological parameter values r, q and K, we obtain the dynamic trajectory between biomass and time, indicates that at the first year of observation, the biomass level is relatively high, but when several years later the biomass tends to decrease until it reaches the stable of biomass obtained for about 30 years and so on (t > 30).

Georgina Maria Tinungki* AbstractThere are some tests proposed for un-nested hypothesis between J-Davidson Test and MacKinnon Test. J’s Test is often bad result, but it always works very well when used bootstrap. Bootstrapping for J’s Test is expected to be able to show that by using bounded sample is better, because there is no fault in counting process. Moreover, bootstrapping J-Test will omit the possibility of inconsistence of the results test previously. Simulation result of Monte Carlo will compare the proposed bounded sample test with Cox and J’s Test previously. Keywords: un-nested hypothesis, J-Davidson Test, MacKinnon Test AbstrakTerdapat beberapa pengujian yang diusulkan untuk hipotesis tak tersarang antara lain Uji-J Davidson dan MacKinnon. Uji-J sering bekerja buruk, tetapi biasanya bekerja sangat baik ketika dibootstrapkan.. Bootstrapping Uji-J diharapkan mampuh menunjukkan sampel terbatas lebih baik karena tidak mempunyai kesalahan didalam proses perhitungan. Lebih dari itu, bootstrapping J-Tests akan mengeluarkan kemungkinan dari ketidak konsistenan hasil uji yang sebelumnya. Hasil Simulasi Monte Carlo membandingkan uji sampel terbatas yang diusulkan dengan test yang sebelumnya seperti Uji Cox dan J-Test. Kata Kunci: Hipotesis tak tersarang,, Uji-J Davidson, Uji MacKinnon

Sparse Principal Component Analysis ( Sparse PCA) is one of the development of PCA. Sparse PCA modifies new variables as a linier combination of p old variables (original variable) which is yielded by PCA method. Modifying new variables is conducted by producing a loading yang sparse matrix, such that old variable which is not effective (value of loading is zero) able be exit from PCA. In this study, Sparse PCA method was applied on data of Indonesia Poverty population in 2015, that contains 13 variables and 34 observation with variable reduction such that yields 4 (four) new variables, which can explain 80.1% of total variance data. This study show, the loading matrix that has been yielded by using Sparse PCA method to become sparse with there exist 11 elements ( loading value) zero entry of matrix, such that the model that has been produced to be simpler and easy to be interpreted. Keywords: Principal Component Analysis, Sparse Principal Component Analysis, reduksi dimensi, matriks loading yang sparse Abstrak Sparse Principal Component Analysis ( Sparse PCA) merupakan salah satu pengembangan dari metode PCA. Sparse PCA memodifikasi variabel-variabel baru yang merupakan kombinasi linear dari variabel lama (variabel asli) yang dihasilkan oleh metode PCA. Pemodifikasian variabel baru ini dilakukan dengan dengan menghasilkan matriks loading yang sparse sehingga variabel lama yang tidak efektif (memiliki nilai loading sama dengan nol) dapat dikeluarkan dari model PCA. Pada penelitian ini, metode Sparse PCA diterapkan pada data Indikator Kemiskinan Penduduk Indonesia Tahun 2015 yang memuat 13 variabel dan 34 observasi dengan reduksi variabel menghasilkan 4 (empat) variabel baru yang telah mampu menjelaskan 80,1% dari total variansi data. Hasil penelitian menunjukkan, matriks loading yang dihasilkan menggunakan metode Sparse PCA menjadi sparse dengan terdapat 11 elemen (nilai loading ) matriks bernilai nol sehingga model yang dihasilkan menjadi lebih sederhana dan mudah untuk diinterpretasikan. Kata Kunci : Principal Component Analysis, Sparse Principal Component Analysis , reduksi dimensi, matriks loading yang sparse

Ketidaksamaan Cramer–Rao merupakan batas bawah dari variansi suatu penaksir tak bias. Suatu penaksir tak bias mempunyai variansi yang sama dengan batas bawah yang berarti bahwa penaksir tersebut mempunyai variansi terbaik dalam kumpulan besar dari penaksir.

The law of large numbers is a statistical concept that calculates the average number of events or risks in a sample or population to predict something. The larger the population is calculated, the more accurate predictions. In the field of insurance, the Law of Large Numbers is used to predict the risk of loss or claims of some participants so that the premium can be calculated appropriately. For example there is an average that of every 100 insurance participants, there is one participant who filed an accident claim, then the premium of 100 participants should be able to provide Sum Assured to at least 1 accident claim. The larger the insurance participant is calculated, the more precise the prediction of the calendar and the calculation of the premium. Life insurance, as a tool for risk spread, can only work if a life insurance company is able to bear the same risk in large numbers. Here apply what is called the law of large number. The law of large numbers states that if the amount of exposure to losses increases, then the predicted loss will be closer to the actual loss. The use of the law of large numbers allows the number of losses to be predicted better.