Goodness-of-fit Test Research Articles

Dentists’ Awareness about the Spread of COVID-19 Infection Through Oral Cavity and Role of Saliva as a Diagnostic Tool for the Same

Background and Aim An outbreak of pneumonia was announced in Wuhan China in December 2019 and its causative factor was classified as a new Coronavirus. Saliva appears to be a promising tool and an alternative to nasopharyngealoropharyngeal swabs for COVID-19 diagnosis and monitoring. The present study aimed to assess the awareness of the dentists regarding the spread of COVID-19 infections through the oral cavity and the importance of the saliva as a diagnostic tool.Methods This cross-sectional survey was conducted including 400 subjects and an online Google form was used to collect the data. The questionnaire consisted of 15 questions among which five were related to knowledge five were attitude based questions and remaining five questions were related to practice.Chi Square Goodness of Fit test was used to compare the distribution of responses to knowledge attitude and practice based questions.Results Majority 82.7 of the facultypractitioners had a fair knowledge about the spread of COVID-19 infection and saliva as an identification tool. Nearly 76.6 preferred hand scaling during the pandemic. Nearly 69.6 of participants reported to have felt anxious while treating patients during the pandemic. Nearly 74.3 participants believed that Coronavirus can be disinfected using 0.1 sodium hypochlorite. Around 78 participants believed use of high volume salivary ejection as a safe dental practice.Conclusion In the present study facultypractitioners demonstrated fair knowledge regarding the role of oral fluids in the spread of COVID-19 infection and also on the role of saliva as a diagnostic tool for the same.

Analysis of Linear Log Models on Covid-19 Data in Indonesia

Covid-19 is still a concern of the world, including Indonesia. The transmission of Covid-19 is very fast and has a wide impact on all people around the world, especially Indonesia. In everyday life, we find a lot of data that looks into a certain category. Categorical analysis of data can be done using the log linear model. The log linear model is used to analyze the relationship between categorical variables that form a contingency table of arbitrary dimensions. The analysis used in this study is to make descriptive statistics and three-way contingency tables, then perform the analysis with the help of SPSS 25.0 software where the goodness of fit test is used to see which models can be used or suitable. The purpose of this study is to analyze a log linear model, so that a log linear model is obtained that is suitable for Covid-19 data based on gender, province, and age group. The conclusion of this study is that of the 9 models used, the model is the most suitable model to be used, with a value of 18,885 and the equation of the log linear model is , which means that there is a relationship between the two factors for the variables gender and province, gender and age group and province and age group in Covid-19 cases in Covid-19 in Indonesia by gender, province, and age group.

Assessment of uncertainty of error in design flood estimates using 2-parameter log normal distribution

Estimation of design flood is formed as a basis for planning and management of civil engineering construction especially hydraulic structures such as water use and flood control structures. The dimensions and capacity of the buildings depend on flood magnitude for a given return period that may have some uncertainty due to model error. This can be achieved through Flood Frequency Analysis (FFA) that involves fitting probability distribution to the series of Annual Maximum Discharge (AMD) data. In this paper, a study on assessment of uncertainty of error in design flood estimates was carried out by fitting Method of Moments (MoM), Maximum Likelihood Method (MLM) and method of L- Moments (LMO) of 2- parameter Log Normal (LN2) distribution to the series of AMD data for river Tapi at Prakasha barrage. The selection of best fit method of LN2 was made through Goodness-of-Fit (GoF) tests viz., Chi-Square and Kolmogorov-Smirnov, and Model Performance Indicators (MPIs) such as correlation coefficient, root mean absolute error and root mean squared error. GoF tests results confirmed the suitability of all three methods of LN2 for estimation of Peak Flood Discharge (PFD). The results indicated that the predicted PFD by MLM is closer to the observed data. From FFA results, it was witnessed that the standard error on the estimated PFD by MoM is minimum than those values of MLM and LMO. The outcomes of FFA results were weighed with MPIs and found that MLM is better suited amongst three methods of LN2 for estimation of PFD at Prakasha barrage.

STUDIES ON INHERITANCE OF BACTERIAL WILT RESISTANCE IN TOMATO CROP CAUSED BY Ralstonia solanacearum (E.F. SMITH)

The study aimed at the inheritance of bacterial wilt resistance in tomato with six different generations (P1, P2, F1, F2, BC1 and BC2) obtained by 4 crosses (Susceptible X Resistant). Two susceptible cultivars (Arka Meghali and Arka Vikas) and two resistant lines (H-7996 and H-7997) of tomato were crossed during kharif , 2020 in four possible combinations to generate the experimental material. The P1, P2, F1, F2 BC1 and BC2 generations of 4 crosses were screened for bacterial wilt caused by Ralstonia solanacearum (E.F. Smith) in tomato research fields of Monsanto Holdings Private Ltd., Kallinayakana Halli village, Gauribidanuru Taluka, Chikkaballapura District, Karnataka to understand the mode of inheritance. Single dominant gene controlling the resistance to bacterial wilt was observed in two crosses between Arka Meghali x H-7996 and Arka Vikas x H- 7997. Chi-square test for goodness of fit indicated that the F2 is segregating in the ratio 3 resistant to 1 susceptible in the single gene model. Resistant to susceptible ratio in BC2 was 1:1. While, in the crosses between Arka Vikas x H-7996 and Arka Meghali x H-7997 the complimentary gene interaction was observed with the F2 expected ratio of 9:7 indicating duplicate recessive epistasis.

Robust Estimates for One-Parameter Exponential Regression Model

One-parameter exponential regression is one of the most common and widely used models in several fields, to estimate the parameters of the one-parameter exponential regression model use the ordinary least square method but this method is not effective in the presence of outlier values, so robust methods were used to treat outlier values in the one-parameter exponential regression model are to estimate the parameters using robust method (Median-of-Means, Forward search, M-Estimation), and the simulation was used to compare between the estimation methods with different sample sizes and assuming four ratios from the outliers of the data (10%, 20%, 30%, 40%). And the mean square error (MSE) was made to reach the best estimation method for the parameters, where the results obtained using the simulation showed that the forward search is the best because it gives the lowest mean of error. On the practical side, expenditure and revenue data were used to estimate the parameters of the one-parameter exponential regression, where the data was tested, it appeared to have an exponential distribution, and the boxplot and (COOK) test were used to detect the outliers present in the real data. The Goodness of fit test was used for the one-parameter exponential model, and it was found that the data did not follow the normal distribution, and it was found that it suffers from the problem of heterogeneity of variance. The one-parameter exponential regression model for the expenditure and revenue data was estimated using the advanced search method because it was the best estimate.
 Paper type Research paper

Dynamic Robust Spectrum Sensing Based on Goodness-of-Fit Test Using Bilateral Hypotheses

Dynamic spectrum detection has attracted increasing interest in drone or drone controller detection problems. Spectrum sensing as a promising solution allows us to provide a dynamic spectrum map within the target frequency band by estimating the occupied sub-bands in a specific period. In this paper, a robust Student’s t-distribution model is built to tackle the scenario with a small number of observed samples. Then, relying on the characteristics of the statistical model, we propose an appropriate goodness-of-fit (GoF) test statistic regarding a small number of samples. Moreover, to obtain a reliable sensing, bilateral hypotheses of the test statistic are both used to make a decision. Numerical simulations show the superiority of the proposed method compared with other schemes, including the unilateral hypothesis-based GoF testing and the conventional energy detection, in a small number of sample cases.

PENGARUH TINGKAT SUKU BUNGA, LEVERAGE, PROFITABILITAS, DAN LIKUIDITAS TERHADAP RETURN SAHAM

This study aims to analyze the influence of interest rates, leverage, profitability, and liquidity. This research was conducted at the Indonesia Stock Exchange in Banking Sector companies. The research design used in this study is quantitative. The sample uses the Purposive Sampling formula, namely 30 issuers, which are included in the banking sector companies that go public on the Indonesia Stock Exchange with an observation period of 3 (three) years from 2018 to 2020, so the amount of data used is 90 data. The analysis technique in this study used descriptive-comparative statistics, namely: the classical assumption test, multiple linear regression analysis, and the goodness of fit test. The results of the analysis show that 1) the interest rate has no effect on stock returns. 2) leverage has a positive and significant effect on stock returns. 3) profitability has a positive and significant effect on stock returns. 4) liquidity has a positive and significant effect on stock returns. This research provides considerations to investors in investing in the Indonesia Stock Exchange, especially in the banking sector.

Validating the predictive precision of the dialogue support tool on Danish patient cohorts

BackgroundDespite advances in surgical techniques and diagnostics, some patients remain unsatisfied with the result following spine surgery. One way to improve patient satisfaction may be found in better alignment of expectations. Prognostic tools might prove useful in strengthening surgeon-patient communication prior to surgery. The purpose of this study is to assess the predictive capabilities of the Swedish based Dialogue Support (DS) tool for spine surgery on a Danish population. MethodsThe study included the diagnoses lumbar disc herniation, lumbar spinal stenosis, and lumbar degenerative disc disease. A total of 5.954 patients were retrieved from the Danish national spine registry (DaneSpine). For each group, 200 random cases with complete preoperative and 1 year follow-up data were selected. Two outcome measures were used: Global assessment of pain (GA pain) and satisfaction with outcome. Predictions were produced by manual entry in the DS application. Goodness of fit tests were used to compare the predicted distribution of proportions with successful outcomes (GA pain) to the actual distribution in the three samples. Binomial tests were performed to evaluate the predicted proportion of satisfied patients. Furthermore, ROC-curves, calibration plots, and metrics were calculated to assess the predictive performance. ResultsROC curves showed comparable AUC values with the values reported by the developing authors of the DS from 0.62 to 0.73 (GA pain) and 0.64 to 0.70 (satisfaction with outcome). The calibration plots, however, revealed a low degree of concordance. For GA pain sensitivity varied from 92.4% to 99.3%, and specificity from 1.5% to 13.4%. For satisfaction, sensitivity varied from 97.1% to 99.2% and specificity from 0.0% to 2.9%. ConclusionsThe predictive capabilities of the DS tool could not be generalized to the Danish sample cohorts. Further research on larger samples, provided full access to the underlying algorithms can be obtained, could produce a different result.

Managing Geotechnical Uncertainty With Simulation Models: An Introduction

In a standard deterministic analysis discrete scenarios are considered, and a moderately conservative “characteristic” value is used as a design basis. However, fixed or exact values in a real-world geotechnical site seldom occurs. Deterministic approaches may not explicitly consider the ground uncertainty. Simulations using various probabilities provides for this uncertainty as each parameter input is treated as a random variable within certain measured ranges or ability to evaluate. Monte Carlo (MC) sampling is a traditional technique for generating random numbers to sample from a probability distribution. When low probability events occur, a small number of MC iterations might not sample sufficient quantities of these outcomes for inclusion in the simulation model. Latin Hypercube (LH) sampling uses stratification of the input probability distributions, to overcome the limitations of Monte Carlo sampling. The simulation results show low probability outcomes are included in the sampling for the simulation model. At a high number of simulation iterations both provide similar outputs, but at low simulation iterations the LH is more reliable. However, both the MC and LH sampling suffer from impractical values at low or high probability events when the normal probability density function (PDF) is adopted. The normal PDF is commonly used in statistical modelling. Non-normal PDFs often represent the best fit PDF when a goodness of fit test is carried out. The errors associated with using the common normal PDF are shown with the above-mentioned simulation models. This best fit PDF applies whether simulation models as described above is used or even with simple “what if” sensitivity models in traditional analysis.

ANALYSIS DETERMINANTS OF INDONESIAN PALM OIL EXPORT VOLUME TO TEN MAJOR DESTINATION COUNTRIES

This research is entitled Analysis of Determinants of Indonesian Palm Oil Export Volume to Ten Major Destination Countries. Economic growth also varies among these countries. India's economy grew steadily with an average annual growth rate of 5.2%, which is likely to increase its demand for palm oil. China's economy, despite a slight decline in 2020 due to COVID-19, rebounded with an average annual growth of 3.9%. This study aims to analyze the determinants of Indonesia's palm oil export volume to ten major destination countries during the period 2012-2022 using a descriptive quantitative approach with panel data that combines time series data and cross-sectional data. Secondary data was obtained from various sources such as BPS, Investing.com, World Bank, and Macrotrends. The analysis method used includes panel data regression with the selection of the best model through Chow and Hausman tests, as well as classical assumption tests and goodness of fit tests. The results show that the international price of palm oil has a positive effect on export volume, while the exchange rate, economic growth of importing countries, and inflation in Indonesia have a negative effect. Overall, these economic variables have a significant effect on Indonesia's palm oil export performance. This study model indicates that the independent variables (international prices, exchange rate, economic growth, and inflation) collectively explain 94.10% of the variation in Indonesian palm oil export volume. This indicates that these economic factors exert a strong influence on the performance of Indonesian palm oil exports.

The Effect of Science Kit and Supervision Models on The Implementation and Implications on The Evaluation of Science Practicum Distance Learning

The implementation of science practicum is one of the challenges in the open distance education system. The purpose of this research is to examine the effect of the Kit IPA variable and the pattern of guidance on the implementation and evaluation of Science Practicum activities in Distance Learning. The type of research in this study is an explanatory research with a quantitative approach involving a number of 306 students who have taken the Science Practicum course in the Elementary School Teacher Education program. Respondents' responses were analyzed using a Likert scale and data analysis using the Structural Equation Model (SEM). The results of the data have met the Model Suitability Test (Goodness of Fit Test) which includes: Science Kit and Guidance Patterns have a significant effect on Practicum Implementation. Guidance pattern has a significant effect on practicum evaluation, but Kit IPA and practicum implementation have no significant effect on practicum evaluation. The implication of this research is that it provides the potential for improvement in the implementation of science practicum in distance education due to the needs of the educational community.

An information ratio-based goodness-of-fit test for copula models on censored data.

Copula is a popular method for modeling the dependence among marginal distributions in multivariate censored data. As many copula models are available, it is essential to check if the chosen copula model fits the data well for analysis. Existing approaches to testing the fitness of copula models are mainly for complete or right-censored data. No formal goodness-of-fit (GOF) test exists for interval-censored or recurrent events data. We develop a general GOF test for copula-based survival models using the information ratio (IR) to address this research gap. It can be applied to any copula family with a parametric form, such as the frequently used Archimedean, Gaussian, and D-vine families. The test statistic is easy to calculate, and the test procedure is straightforward to implement. We establish the asymptotic properties of the test statistic. The simulation results show that the proposed test controls the type-I error well and achieves adequate power when the dependence strength is moderate to high. Finally, we apply our method to test various copula models in analyzing multiple real datasets. Our method consistently separates different copula models for all these datasets in terms of modelfitness.

The Effect of Over-Differencing on Model Validity

We examine the effect of unnecessary differencing (over-differencing) on the appropriateness of the proposed models (Autoregressive of order one AR(1), Autoregressive of order two AR(2), and Moving Average of order two MA(2)). Our interest arises from the fact that in practical applications the fitted model due to inappropriately differenced data can still suitably describe the data sample based on the goodness of fit test using residual analysis. Given that we use simulation study to detect the consequences of unnecessary differencing on the fitted model. While the simulation study can be controlled using different scenarios, it becomes more challenging when dealing with real data. The validity and performance of the fitted models was checked by observing the changes in the estimated coefficients, the associated standard errors (SE), the residual variance, and Akaike information (AIC) by comparing them with the true parameters of the system (true model). The uniqueness of this paper is to examine how the fitted model is sensitive (valid) to the over-differencing.

PILIHAN PEMBIAYAAN KESEHATAN MASYARAKAT DI KECAMATAN DENPASAR SELATAN KOTA DENPASAR PROVINSI BALI

The problem that often occurs in the case of additional insurance markets is the existence of imperfect information which results in the market being unable to allocate resources efficiently. Because the public has the power to decide whether or not to buy an additional insurance product, policies for the provision of public health services through the redistribution of wealth through the insurance industry are susceptible to adverse selection problems. The research objective to be achieved is to find out whether adverse selection occurs in the additional insurance market in South Denpasar District. This research was conducted in South Denpasar District with a total sample of 100 people. Variables used in this study, namely additional insurance, chronic conditions, self-care activities, log family income, and partner status through interviews and distribution of questionnaires. The collected data were then analyzed using binary logistic analysis techniques, goodness of fit tests, and partial tests (t-test). The results of the study show that chronic conditions have a partial positive effect on Additional Insurance in South Denpasar District. Self Care Activity does not have a partial positive effect on Additional Insurance in South Denpasar District. Family Income Log has a partial positive effect on Additional Insurance in South Denpasar District. Spouse status has no partial positive effect on Additional Insurance in South Denpasar District.

Mediasi Biaya Keagenan pada Pengaruh Kebijakan Utang dan Kebijakan Dividen terhadap Nilai Perusahaan-perusahaan Sektor Barang Konsumen Primer yang Terdaftar dalam Indeks Idxnoncyc Bursa Efek Indonesia Periode 2018-2020

The primary purpose of establishing a company is to maximize its value of the company. In that process, firm value is inseparable from agency problems caused by shareholders' and managers' different interests. Several mechanisms are expected to reduce agency problems, including debt and dividend policies. However, until now, the role of these two policies in solving agency problems is still inconsistent. This study aimed to determine how agency costs mediate debt and dividend policy on firm value. The fundamental theories to solve this research problem are agency and free cash flow theories. The object of this study is to the primary consumer goods sector companies listed on the IDXNONCYC index on the Indonesia Stock Exchange in the 2018-2020 period, with a total final sample of 25 companies or 75 units of analysis. The data analysis technique used in this study is the path coefficient test using SmartPLS 3.2.9. With this technic, the authors analyze the outer model test, inner model test, and the goodness of fit test. The outer model test with bootstrapping outer weights has an invalid indicator, namely the PBV indicator. Therefore this indicator needs to be eliminated. The goodness of fit states that the model is significant but has a weak model. This study found that debt and dividend policy positively affect firm value and negatively affect agency costs. Agency costs have a positive and significant effect on firm value. Agency costs have no mediation effect of debt and dividend policy on firm value.

Some goodness of fit tests based on centre outward spacings

Data depth provides a centre-outward ordering for multivariate data. Recently, some univariate goodness of fit (GoF) tests based on data depth have been studied. This paper discusses some univariate GoF tests based on centre-outward spacings. These tests have similar asymptotic properties as those based on usual spacings.

Goodness-of-Fit Tests in Calibration: Are They Any Good for Selecting Least-Squares Weighting Formulas?

No. Most goodness-of-fit (GOF) tests attempt to discern a preferred weighting using either absolute or relative errors in the back-calculated calibration x values. However, the former are predisposed to select constant weighting and the latter 1/x2 or 1/y2 weighting, no matter what the true weighting should be. Here, I use Monte Carlo simulations to quantify the flaws in GOF tests and show why they falsely prefer their predisposition weighting. The weighting problem is solved properly through variance function (VF) estimation from replicate data, conveniently separating this from the problem of selecting a response function (RF). Any weighting other than inverse-variance must give loss of precision in the RF parameters and in the estimates of unknowns x0. In particular, the widely used 1/x2 weighting, if wrong, not only sacrifices precision but even worse, appears to give better precision at small x, leading to falsely optimistic estimates of detection and quantification limits. Realistic VFs typically become constant in the low-x, low-y limit. Thus, even when 1/x2 weighting is correct at large signal, the neglect of the constant variance component at small signal again gives too-small detection and quantification limits. VF estimation has been disparaged as too demanding of data. Why this is not true is demonstrated with Monte Carlo simulations that show only a few percent increase in calibration parameter uncertainties when the VF is estimated from just three replicates at each of six calibration x values. This point is further demonstrated using examples from the recent literature.

Discharge of pedagogic duties: a bootstrapped structural equation modelling of teachers’ use of research materials in school libraries

A large body of literature focuses on teachers’ service delivery and other related concepts. However, most of them have been judged to be generic. Measuring teachers’ service delivery generally does not picture events in specific areas. This study used a bootstrapped structural equation modelling to analyse how teachers’ use of library research materials (ULRMs) predicts teachers’ discharge of pedagogic duties across seven areas. A structured questionnaire collected primary data from 2,406 teachers and 7,218 students who consented and participated voluntarily. Exploratory and confirmatory factor analyses were used for dimensionality and goodness of fit tests. Convergent and discriminant validities were achieved based on average variance extracted (AVE) and squared correlation. The internal consistency measures were Cronbach’s alpha, McDonald’s Omega, and composite reliability. Results of the study showed among others that the teachers’ ULRMs is a significant direct predictor of teachers’ lesson preparation (β = .48, p < .001), use of instructional materials (β = .53, p < .001) and instructional delivery (β = .20, p < .001). Teachers’ lesson preparation had a significant direct effect on their use of instructional materials (β = .39, p < .001), classroom management (β = .19, p < .001), instructional delivery (β = .45, p < .001) and lesson evaluation (β = .29, p < .001). Significant mediation effects were recorded on the link between the ULRMs and teachers’ pedagogic duties. This study has empirically proven that teachers’ use of library research materials is crucial for the instructional process. It serves as the information bank in schools and as a source of instructional materials to enabling educational practitioners to plan and deliver practical lessons. Highlights: Teachers’ discharge of pedagogic duties is an anchor point in secondary education. Being able to evaluate lessons is associated with being able to provide feedback. Teachers effectively utilised instructional materials, having planned how to use them. Teachers’ lesson preparedness effectively decided their choice of instructional strategy.

Imperfect repair models: Sequential goodness‐of‐fit testing based on predictive performances

AbstractImperfect repair (IR) modelling has attracted a lot of attention during the last decades. To assess and predict repairable systems' reliability, analysts have to choose among a plethora of models: renewal processes, non‐homogeneous Poisson processes (NHPPs), Brown–Proschan, Kijima, ARA, ARI, Quasi‐Renewal (QR), Trend‐Renewal and so forth. Choosing an appropriate model for a given failures, dataset is an important practical issue. The fit of an IR model can be assessed using goodness‐of‐fit (GoF) tests but very few have been proposed and a good fit to past data does not guarantee good reliability predictions. This work proposes general GoF tests for IR models based on sequential (or on‐line) assessment of times‐to‐failures forecasts. The suggested predictive‐sequential (or prequential) GoF tests have a low computational complexity and a common test statistic for several IR models. These tests have been proved to be asymptotically distribution‐free (ADF) for renewal and power‐law processes (PLPs). Our numerical simulations and preliminary theoretical results highly suggest that this ADF property still holds for several other IR models. The prequential tests are much easier to use than the already known bootstrap tests, and a simulation study shows that they are also slightly more powerful. The simulations also show that the prequential tests are powerful to identify classes of similar appropriate models but are much less powerful to distinguish models belonging to the same class. A comparison of MTTF estimates show that models from the same class give close reliability predictions and that the tests are able to reject models which would yield to dramatic errors in reliability predictions.

Viable forecasting monthly weather data using time series methods

The main object of the research was to assess the forecast values of the weather parameters by using three-time series methods such as Decomposition of time series, Autoregressive (AR) model with seasonal dummies and Autoregressive moving average (ARMA) /Autoregressive Integrated moving average (ARIMA) model. A recent phenomenon in weather changing has disturbed the world in general and Pakistan in particular. In Pakistan due to climate change, flood and heat stroke have taken many lives. Stationarity was measured through the Augmented Dickey-Fuller test; results showed that some variables are I(0) and some are I(1). The reliability of the forecast results was examined through the goodness of fit test. For finding the best fit model, the performance measures of various models: Root Mean Squire Error, Mean Absolute Error and Mean Absolute Percentage Error were considered. The model in which the above statistics are the minimum was chosen as the appropriate model. After model analysis and validation, it was observed that AR-model with seasonal dummies was found to be the best fit model between the three models. Meanwhile, the forecasting for the period Jan.2018 to Dec.2018 was made based on the best fit model. Given the future forecasting results, the temperature will be normal at selected stations. The wind and rainfall will also be present. Overall, it was suggested that the obtained findings of meteorological stations' weather might be normal for the coming few months over there, and no chance of heatstroke and flood might be expected. Future studies must be carried out to provide the awareness to well-being regarding ecological hazardous to minimize their economic loss through mass media.