Model Adequacy Tests Research Articles

Optimization of dabi teff-field pea based energy and protein dense novel complementary food with improved sensory acceptability using D-optimal mixture design

Protein-energy malnutrition is unacceptably high among children in developing countries due to inadequate required nutrients and poor quality of complementary foods characterized by low protein and energy density and often monotonous. Thus, this research was aimed at examining the potential of including dabi teff, the underutilized/forgotten crop into pre-processed local food crops viz., germinated maize, roasted barley, roasted field pea, dehulled oats and linseed to develop energy and protein-dense optimized novel complementary food with improved sensory acceptability. Nutrisurvey software was employed to define ranges and they were constrained at 20–35% dabi teff, 0–30% field pea and 5–20% maize, while the rest were set constant at 25% barley, 15% oats and 5% linseed. Eleven experimental runs were generated from the six mixture components using D-optimal mixture design, Stat-Ease Design Expert ® software version 11. A 5-point Hedonic scale was used to evaluate the sensory attributes. ‘Scheffe’ regression was used to fit and test model adequacy and numerical multi-response optimization was performed to identify optimal points using the Design expert. Field pea and linseed contained significantly higher (P < 0.05) protein at 20.95% and 20.57%. The newly formulated products contained significantly higher protein (1.4–1.6 times) and protein density (1.31–1.56 times) as compared to the control and fulfilled the recommended standard. The optimal was identified at 34.66% dabi teff, 25% barley, 15% oats, 15.34% field pea, 5% linseed and 5% maize flour ratios with response values at overall optimization to be 5.57% moisture, 15.74% protein, 5.09% fat, 2.26% ash, 2.88% fiber, 73.05% carbohydrate, 380.43 kcal/100 g energy and 4.12 sensory acceptability score and it contained an energy density of 1.27 kcal/g and protein density of 4.14 g/100kacl. These findings showed that optimized dabi teff-field pea based novel complementary food can be used as a sustainable food-based strategy to combat protein-energy malnutrition among children in developing countries.

Parametric optimization and wear analysis of AISI D2 steel components

AISI D2 steel is widely used for forming die or cold stamping, chipper knives, shear blade, trimming cutter threading dies, punches and stamping tools etc. Hence, the current investigation aim is to determine the effects of sliding parameters on the wear performance of AISI D2 steel. The experiments were conducted on AISI D2 steel pin and disk of mild steel using the pin-on-disk apparatus. After that, relationships for forecasting weight loss of pins due to rubbing action have been developed using the design of experiment (DOE), which is based on response surface methodology (RSM) and has three independent parameters (slide duration, normal load, and sliding speed). ANOVA was used to perform the model adequacy tests, and the results of various parameters were computed and displayed in 3D surface graphs and contour plots. To reduce the weight loss, numerical optimization has been done while keeping all of the input parameters within range (wear volume). The manufacturing sector that uses AISI D2 steel for deforming dies would benefit from the study's findings.

Paleobuddy: An R package for flexible simulations of diversification and fossil sampling

Abstract Simulations are powerful tools for investigating evolutionary questions. Statistical methods for the analysis of diversification and fossil sampling rates require extensive testing through simulation. Simulations also aid in the investigation of evolutionary dynamics that lack analytical solutions, besides model adequacy tests. Flexible simulation of fossil records and phylogenies is a necessary tool to fully investigate diversity through time. paleobuddy is an R package that aims to fill several gaps between current simulators by providing a more general framework, with flexible, robust and independent simulations of birth–death processes, fossil records and phylogenetic trees, while allowing for straightforward implementation of novel scenarios. Here we describe the main algorithms involved in paleobuddy simulations, give a summary of the capabilities of the package in terms of evolutionary scenarios and visualization and illustrate a paleobuddy workflow. paleobuddy expands the available parameter space to investigate complex inference models. Given that it allows for simulations of phylogenies (complete, reconstructed or ultrametric) and fossil records from the same underlying birth–death simulations, it can increase our understanding about the differences in palaeontological and neontological approaches, and consequently expand our capacity of analysing a combination of evidence from extinct and extant species. Finally, the focus on birth–death simulations with budding speciation events allows for more generality in the diversification processes considered, expanding the flexibility and capabilities of simulation in the field.

A new automatic forecasting method based on a new input significancy test of a single multiplicative neuron model artificial neural network

ABSTRACT The model adequacy and input significance tests have not been proposed as features for the specification of a single multiplicative neuron model artificial neural networks in the literature. Moreover, there is no systematic approach based on hypothesis tests for using single multiplicative neuron model artificial neural networks for forecasting purposes like classical time series forecasting methods. In this study, new methods are proposed to solve these problems. The performance of the proposed test procedures is investigated in a simulation study. According to simulation results, the proposed tests have very good performance. Moreover, the test procedures are illustrated by using two real-world examples. The second contribution of the paper is that an automatic forecasting method is proposed based on input significance and model adequacy tests and the particle swarm optimization-based learning algorithm. The proposed automatic forecasting method is applied to M4 competition hourly data sets, and it is the best pure machine learning method among others in the competition. The proposed automatic forecasting method is more accurate than all benchmarks, such as MLP, RNN, and ETS, which were proposed by the competition organizers.

Designing an Informal Payment Model for Patients Admitted in the Iran Health System: A qualitative Study.

Countries rely on out-of-pocket spending to different degrees and employ varying techniques. This study aimed at designing an out-of-pocket payment model of patients admitted to the Iran health system. This study was a combined qualitative and quantitative study. The statistical population of the qualitative section was 30 experts who were purposefully selected and continued by snowball method until data saturation, and in the quantitative section were 212 managers of the Ministry of Health. The questionnaire was designed based on qualitative analysis. Content validity was confirmed based on expert opinion and structural validity using exploratory factor analysis and confirmatory factor analysis. Reliability was confirmed using CRONBACH's alpha coefficient and composite reliability. For model adequacy, KMO index and Bartlett test were used, and for model fit, CFI and IFI fitness index were used. Based on the results of 6 main themes, 20 Concept and 120 sub-themes of out of pocket payment of hospitalized patients were extracted. The value of chi-square was 4599.861, the degree of freedom was 2421 and the result of their ratio was 1.899 in the model, which was an acceptable value. CFI and IFI fitness indicators are acceptable. The SRMR index was 0.1153, which indicates the adequacy of the model. The findings showed that the main dimensions of out of pocket payment of hospitalized patients include causal factors, underlying factors, intervening factors, pivotal categories, strategies and consequences. Therefore, the use of a paradigm model to pay attention to all the effective dimensions in reducing the payment of hospitalized patients is recommended.

Model adequacy tests for probabilistic models of chromosome-number evolution.

Chromosome number is a central feature of eukaryote genomes. Deciphering patterns of chromosome-number change along a phylogeny is central to the inference of whole genome duplications and ancestral chromosome numbers. ChromEvol is a probabilistic inference tool that allows the evaluation of several models of chromosome-number evolution and their fit to the data. However, fitting a model does not necessarily mean that the model describes the empirical data adequately. This vulnerability may lead to incorrect conclusions when model assumptions are not met by real data. Here, we present a model adequacy test for likelihood models of chromosome-number evolution. The procedure allows us to determine whether the model can generate data with similar characteristics as those found in the observed ones. We demonstrate that using inadequate models can lead to inflated errors in several inference tasks. Applying the developed method to 200 angiosperm genera, we find that in many of these, the best-fitting model provides poor fit to the data. The inadequacy rate increases in large clades or in those in which hybridizations are present. The developed model adequacy test can help researchers to identify phylogenies whose underlying evolutionary patterns deviate substantially from current modelling assumptions and should guide future methods development.

Students’ Satisfaction on Training cum Workshop Programme on Thesis Writing and General Research Skill Development

Faculty of Management, Tribhuvan University's Master of Business Studies level is not fully based on a research course that's why for many students thesis writing task is being the first attempt in their academic life. This study aims to determine students' satisfaction with the training cum workshop programme on their thesis writing and general research skills development and to assess the model that determines general satisfaction with the programme. Results showed that the participants are only satisfied to some extent with the training cum workshop programme. It was discovered that students show their disagreement on time allocated for every session, about following the time schedule by resource persons, resource persons’ use of practical ways, examples, and exercises in the session. However, they felt the method of thesis format, American Psychological Association styles of citation, and referencing are useful and the programme is going to help them more productively in near future. The step-wise regression model has also been used to set the model. The fitted model has been found to be significant. The model adequacy test has also been performed which shows the data satisfied the assumption of multicollinearity, normality of residuals, and homoscedasticity while no outliers were identified. The study between trained students and non-trained students' perceptions after the completion of their dissertation work can be carried out by future research. Likewise, a longitudinal study before the programme and after the thesis work can be performed in this area too.

Multinomial Logistic Regression Model to Identify Factors Associated with Food Insecurity in Rural Households in Nepal

Background: Food is basics of our lives and many people experiences food insecurity at some time because of food deprivation and lack of access to food due to different resource constraints. It is a global challenge and threatens the rural people in developing countries like Nepal.&#x0D; Objective: The objective of the study is to identify the factors associated with food insecurity in rural area of Nepal.&#x0D; Materials and Methods: The analysis is based on rural household data extracted from the data of Nepal Demographic and Health Survey 2016. The dependent variable food insecurity status was measured in four levels namely food secure, mildly food insecure, moderately food insecure and severely food insecure household using Household Food Insecurity Access Scale. Independent variables were categorical and quantitative variables. In order to identify the factors associated with food insecurity, ordinal logit model was fitted initially. Due to violation of test of parallel lines by overall as well as some of the independent variables, multinomial logistic regression model was finally adopted by examining the model adequacy test.&#x0D; Results: The fitted multinomial logistic regression satisfied the diagnostic test including tests of goodness of fit, multicolinearity diagnostic criteria and minimum criteria of utilization of the model with about 29% predictive power. The variables ecological region, wealth index, size of agriculture land, any member(s) having saving account in any financial institution, any member(s) had gone to foreign employment in last 5 years other than India, family size, number of members completed secondary education and household member rearing cattle(s) were found to be significant. The poorest households (HHs) had 3.14 (CI: 1.88-5.26) times, poorer HHs 2.51 (CI: 1.55-4.07) times and moderate HHs 1.42 times higher chances of being severely food insecure relative to rich HHs.&#x0D; Conclusion: The study revealed that food insecurity of the rural HHs increases with decrease in the wealth index, size of land and number of members of the HHs with completed secondary education. The food insecurity of the households decreases with increase in the access to bank service.

Binary Logistic Model to Identify the Factors Associated with Households with Bank Accounts in Nepal

Introduction: Banks play an important role in ensuringthe economicand social stability, and the sustainablegrowth of the economy. The savings and other accounts in financial institutions, including banks, finances, microfinances and cooperatives, enable people to execute important financial functions. Thus, households that have accounts in any of financial institutions can have access to various banking services.&#x0D; Objective: The objective of the study is to identify the factors associated with households having bank accounts in Nepal.&#x0D; Methods: The analysis is based on household data extracted from the dataset of Nepal Demographic and Health Survey, 2016. The dependent variable is dichotomous, as the households with bank accounts and without bank accounts in any formal financial channels. In order to identify the factors associated with households receiving financial services in Nepal, multiple logistic regression models were developed by examining the model adequacy test.&#x0D; Results: The study finds that a total of 66.9% of the households had bank accounts. Several variables were found to be 1% of significance level. The predictive power of the model is found to be 31.2% and multicollinearity among the independent variables was absent. The Hosmer-Lemoshow goodness of fit test revealed that the data were poorly (p-value=0.056) fitted by the model. However, Osius-Rojek goodness of fit test (z=0.11; p-value=0.911), Stukel test (Z=0.683, p-value=0.494), likelihood ratio test (χ2=2770; p-value&lt;0.0001) and area under receiver operating curve (79.8%) revealed that fitted model was good.&#x0D; Conclusion: Multiple logistic regression model revealed that in mountainous and hilly regions, women-headed households have less chances of not having bank accounts compared to the Terai region and men-headed households. The chances of having a bank account in province-2 is even worse than in Karnali and other provinces. The odds of not having bank accounts gradually decreased with the increase in size of agricultural land, wealth index, increase in family size and the number of family members who have completed secondary education.

Modeling colonization rates over time: Generating null models and testing model adequacy in phylogenetic analyses of species assemblages.

A central theme connecting macroevolutionary processes to macroecological patterns is the shaping of regional biodiversity over time through speciation, extinction, migration, and range shifts. The use of phylogenies to explore the dynamics of diversification due to variation in speciation and extinction rates has been well-developed and there are established methods for inferring speciation times from phylogenies and generating its null distributions (as represented by node heights on molecular phylogenies). But inferring colonization events from phylogenies is more challenging. Unlike speciation events, represented by nodes, colonization events could occur at any point along a branch connecting species in the assemblage to the regional pool. We account for uncertainty in identification of colonization lineages and timing of colonization events by using an efficient analytical solution to inferring the distribution of colonization times from an assemblage phylogeny. Using the same solution, we efficiently derive the null distribution of colonization times, which provides us with a general approach to testing the adequacy of a model to describe colonization events into the assemblage. We illustrate this approach by demonstrating how the movement of squamate lineages into Madagascar has been uneven over time, peaking in the early Cenozoic when ocean conditions favored colonization.

Strain hardening analysis and modelling for sintered Al-Cu-TiC preforms with varying process parameters during cold upsetting

In sintered preforms, the presence of porosities are unavoidable; however, subjected to further deformation, preforms not only show closure of pores but as well as hardens the matrix. Both these phenomenon compliments each other in strain hardening effect, eventually its mechanical characteristics. However, in addition to the deformation, the process parameters also play a prominent role in influencing the strain hardening behaviour. Thus, while it is imperative to establish strain-hardening characteristics for the chosen composition under various process parameter influence, the modelling of the same is also inevitable to design preform for the desired characteristics. In the present investigation, Al with 4.5%Cu preforms has been synthesised through powder metallurgy process, in which, the process parameters such as, weight percentage of TiC, aspect ratio, initial density and the lubricant are varied to two different levels. The complete experiment has been formulated using 24 factorial design; however, it is performed in random order. The study is focused on evaluating strain-hardening characteristics of chosen composition under the influence of process parameters. In addition, the process parameters are modelled using Yates algorithm, and the applicability of the model is ensured through regression analysis and model adequacy test.

Buckling Strength of 3-D Printed Thermoplastic Thin Shells: Notes on an Exploratory Study of As-Printed and Reinforced Cases

Additively-manufactured (AM) materials have a defined mesostructure and natural voids which impact their structural stability; thin shells, which do not have the bulk to support or absorb the effects of the variances in properties, are particularly affected. Thin shells are a common feature in many designs, providing good strength-to-weight ratios for many applications, particularly in the aerospace and structural design domains. The use of AM to fabricate thin structures could both expand the use of AM and improve the application space for thin structures in design, but this problem has not yet been widely discussed for buckling cases. This short technical note explored this problem for thermoplastic thin shells fabricated by fused deposition modeling (FDM), providing insight into the problem, some initial experimental results, and discussion of design implications. A designed 2(4−1) factorial experiment was used to study the buckling behavior, examining the impact of wall thickness, material, and two methods for internal reinforcement (soft infill and polyurethane foam). Analysis of variance (ANOVA) (including model adequacy testing and proof of Fisher Assumption validity) was completed on data from two replications (32 total tests), providing useful information on the significance of the factors and their interactions.

Tail risk of electricity futures

This paper compares the in-sample and out-of-sample performance of several models for computing the tail risk of one-month and one-year electricity futures contracts traded in the NordPool, French, German, and Spanish markets in 2008–2017. As measures of tail risk, we use the one-day-ahead Value-at-Risk (VaR) and the Expected Shortfall (ES). With VaR, the AR (1)-GARCH (1,1) model with Student-t distribution is the best-performing specification with 88% cases in which the Fisher test accepts the model, with a success rate of 94% in the left tail and of 81% in the right tail. The model passes the test of model adequacy in the 100% of the cases in the NordPool and German markets, but only in the 88% and 63% of the cases in the Spanish and French markets. With ES, this model passes the test of model adequacy in 100% of cases in all markets. Historical Simulation and Quantile Regression-based approaches misestimate tail risks. The right-hand tail of the returns is more difficult to model than the left-hand tail and therefore financial regulators and the administrators of futures markets should take these results into account when setting additional regulatory capital requirements and margin account regulations to short positions.

The development of a goodness-of-fit test for high level binary multilevel models

Before making inferences about a population using a fitted model, it is necessary to determine whether the fitted model describes the data well. A poorly fitted model may lead to biased and invalid conclusions, resulting in incorrect inferences. Recent studies show the necessity of goodness-of-fit tests for high level binary multilevel models. The focus here was to develop a goodness-of-fit test to use in the model adequacy testing of high level binary multilevel models and to examine, whether the type I error and power hold for the newly developed goodness-of-fit test considering a three-level random intercept model.

Enhancement of Surface Finish by Optimization Technique Employed for Al 6061 Considering Different Parameters Using RSM

The objective of the present work is to analyze the effects of the machining parameters in turning of Al 6061 alloy on the surface roughness parameters. The Design of experiments based on response surface methodology with three numeric factors (cutting speed, feed rate and depth of cut) five level central composite rotatable designs have been used to develop relationships for predicting surface roughness. The surface roughness parameters were measured using surface roughness tester (SURFTEST SJ- 210). The “Design Expert” software has been used for the analysis. A quadratic model and linear Model have been developed which indicates that interaction is present between the machining parameters (speed, feed, depth of cut). Model adequacy tests were conducted using ANOVA table and the effects of various parameters were investigated and presented in the form of contour plots and 3D surface graphs. Numerical optimization was carried out considering all the input parameters within range so as to minimize the surface roughness. The optimal values obtained are cutting speed 187.84 m/min, feed 73.37 mm/min and depth of cut 0.48 mm.The findings of this study would be beneficial to manufacturing industries (Specially in Automobile sector) where surface finishing plays a very important role.

Testing eco-evolutionary predictions using fossil data: Phyletic evolution following ecological opportunity.

Fossil sequences provide observations of phenotypes within a lineage over time and represent essential data for increasing our understanding of phyletic evolution beyond microevolutionary timescales. I investigate if fossil time series of the diatom Stephanodiscus niagarae/yellowstonensis follow evolutionary dynamics compatible with hypotheses for how the adaptive landscape changes when a population enters a new environment. The lineage-which has a remarkably detailed stratigraphic record-invaded Yellowstone Lake immediately after recession of ice from the basin 14,000 years ago. Several phyletic models portraying different types of evolutionary dynamics-both compatible and not compatible with changes in the adaptive landscape following ecological opportunity-were fitted to the fossil times-series of S. niagarae/yellowstonensis. Different models best describe the three analyzed traits. Two of the models (a new model of decelerated evolution and an Ornstein-Uhlenbeck model) capture trait dynamics compatible with an event of ecological opportunity, whereas the third model (random walk) does not. Entering a new environment may accordingly affect trait dynamics for thousands of years, but the effects can vary across phenotypes. However, tests of model adequacy reveal shortcomings in all three models explaining the trait dynamics, suggesting model development is needed to more fully understand the phyletic evolution in S. niagarae/yellowstonensis.

Forecasting the Monthly Rediscounting Rate in the Philippines

Objective: To develop a best model that can forecast the monthly rediscounting rate in the Philippines. Methods/Statistical Analysis: The researcher utilized the numbers from the certified metadata of the BankoSentral ng Pilipinas (BSP) from January 2009 to December 2015 to shape a forecasting model using the modern forecasting method under time series analysis specifically the Box-Jenkins Procedure. For accurateness of calculations, the researcher employs the use of Statistical Software. Furthermore, the researcher sought to find the finest model using the said forecasting scheme from January 2015 to December 2018. Findings: The results show that the series is persistent at a 4% rate from February 2010 to February 2011 while it slightly reduced over eight more months. However, another constancy in the monthly Philippine Rediscounting rate happened from November 2012 to October 2013 having a rate of 3.5%. Moreover, after such constancy the monthly Philippine rediscounting rate continued to increase until December 2015. After the testing the assumptions and diagnostic test of model adequacy, the researcher finally creates a best model that can predict the future values of rediscount rate in the Philippines based on the evaluation criteria in selecting the best model. The “best” model for the monthly Philippine rediscounting rate in terms of its significant factors IR (Inflation Rate), MMR (Money-Market Rate), TBR (Treasury Bill Rate) and PDR (Peso-Dollar Rate) is Monthly Rediscounting Rate(Predicted) = 0.096(IR) – 0.186(MMR) + 0.310(TBR) + 0.09(PDR). This model building can help the government to create a strategy or system to maintain price stability in the country and help the countrymen to access the basic needs for their everyday life and can be applied to pact with balance-of-payments deficits so as to regulate international movements of capital. Applications/Improvements: Outcome from this study can be used as a monetary tool of the BSP to regulate the level of liquidity in the system. Adding observations from the previous series of the monthly Philippine rediscounting rate may help in discovering a better model in forecasting. Keywords: Forecasting, Monthly, Philippines, Rate, Rediscounting

Testing adequacy for DNA substitution models

BackgroundTesting model adequacy is important before a DNA substitution model is chosen for phylogenetic inference. Using a mis-specified model can negatively impact phylogenetic inference, for example, the maximum likelihood method can be inconsistent when the DNA sequences are generated under a tree topology which is in the Felsentein Zone and analyzed with a mis-specified or inadequate model. However, model adequacy testing in phylogenetics is underdeveloped.ResultsHere we develop a simple, general, powerful and robust model test based on Pearson’s goodness-of-fit test and binning of site patterns. We demonstrate through simulation that this test is robust in its high power to reject the inadequate models for a large range of different ways of binning site patterns while the Type I error is controlled well. In the real data analysis we discovered many cases where models chosen by another method can be rejected by this new test, in particular, our proposed test rejects the most complex DNA model (GTR+I+ Γ) while the Goldman-Cox test fails to reject the commonly used simple models.ConclusionsModel adequacy testing and bootstrap should be used together to assess reliability of conclusions after model selection and model fitting have already been applied to choose the model and fit it. The new goodness-of-fit test proposed in this paper is a simple and powerful model adequacy testing method serving such a regular model checking purpose. We caution against deriving strong conclusions from analyses based on inadequate models. At a minimum, those results derived from inadequate models can now be readly flagged using the new test, and reported as such.

OPTIMISATION OF ORGANIC SOLVENT EXTRACTION OF TOTAL CAROTENOIDS FROM BULGARIAN TOMATO PROCESSING WASTE

The quantity of tomato wastes combined with beneficial characteristics of components of the wastes justifies the great interest of researchers and manufacturers in extracting of carotenoids from this low cost material. In this study the response surface (RSM) approach and Box-Behnken design (BBD) were used to explore the possibility of modelling and optimisation the organic solvent extraction of total carotenoids from Bulgarian tomato processing waste (TPW). Bulgarian TPW consists of tomato pomace was used in this study. Dried and ground TPW was plased in the extraction flask and stirred with acetone at various extraction conditions. The extract obtained was vacuum filtered through filter paper and was subjected to spectrophotometrically total carotenoids content determination. A three-variable, three-level BBD of RSM was employed in optimisation the extraction conditions for the highest recovery of total carotenoids from Bulgarian TPW. A response surface quadratic model was developed and statistical analysis was carried out. Data concerning model adequacy tests indicated that the values of R2 and Radj2 for quadratic model were the higest in comparison to the other models. The obtained F-value of 38.65 implies the model was significant (p &lt; 0.05) and could be used for optimisation. The effects of extraction temperature, solvent to solid ratio and extraction time were significant in total carotenoids yield. Total carotenoids content ranged from 9.78 to 25.28 mg/100 g dried TPW. The predicted values of total carotenoids content were closed to the experimental observed values. By use of RSM the optimal extraction conditions were determined as follow: extraction time of 90 min, solvent to solid ratio of 60 mL/g and extraction temperature of 50ºC. The results obtained showed that predicted (28.40 mg/100 g dried TPW) and experimental (28.86±0.92 mg/100 g dried TPW) values of total carotenoids content were not significant different (p &gt; 0.05).

Estimating probability distributions of travel demand on a congested network

We aim to infer the probability density function (pdf) of Origin–Destination (O–D) demand variables using multiple sets of traffic counts over a network. Through integrating statistics and optimization techniques with transportation domain knowledge, we propose an estimation framework based on Generalized Method of Moment (GMM) with both options of exact and approximated estimators. Compared with existing methods for day-to-day O–D matrix estimation, our approach has three unique advantages. First, it is a rigorous statistical method with a capability of incorporating complex traffic network models suitable for a congested network. Second, our estimation framework is flexible to handle a wide variety of probabilistic models. Finally, instead of just providing point estimates, it reveals large sample statistical properties of the proposed estimator, which serve as a theoretical foundation for assessing estimation quality, constructing confidence region and testing model adequacy. Three numerical examples of different scales are accompanied to demonstrate various aspects of the proposed estimation framework.