Number Of Predictor Variables Research Articles

Optimal reconciliation of hierarchical wind energy forecasts utilizing temporal correlation

Independent wind energy forecasts of a wind farm at different time horizons have limited accuracy, and they show disagreement despite relating to the same wind farm. The limited forecast accuracy is attributable to the insufficient information at a particular time horizon of the wind energy time series, whereas applying distinct forecasting methods to several time series of non-identical patterns at different time scales causes disagreement among forecasts. Mutual disagreement among less accurate forecasts negatively impacts the decision-making capabilities in associated power systems activities at distinct time scales. The configuration of time series expressing different time horizons at different levels of a non-overlapped hierarchically aggregated framework manifests a temporal hierarchy. Forecast combination through reconciliation of time series forecasts drawn at different hierarchical levels of temporal hierarchy using any state-of-the-art method facilitates the sharing of diverse information across the hierarchy; consequently, accuracy and mutual agreement of forecasts improve. Such benefits may be further enhanced by embedding intra- and inter-level forecast error correlations in the forecast reconciliation process. However, the forecast error covariance matrix of temporal hierarchy becomes a complex high-dimensional structure while accommodating intra- and inter-level correlations. Estimating such a matrix is challenging since the high-dimensional structure severely impedes the identifiability of model parameters. Besides, in the hierarchical forecast reconciliation process, the number of predictor variables is generally higher than the number of samples. This condition gives rise to a singular covariance matrix, making it non-invertible, and thus obstructs its parameter estimation. This work employs the MinT(shrinkage) covariance matrix estimator that considers all correlations and shrinks the non-diagonal components of the matrix toward zero to avert the complexity and, therefore, the non-identifiability. Additionally, the shrinkage parameter λ of MinT(shrinkage) conveniently obtains the invertible matrix. The case study validates that while incorporating the intra- and inter-level forecast error correlations, MinT(shrinkage) provides competitively accurate and mutually agreed forecasts over other reconciliation methods.

Loss-guided stability selection

AbstractIn modern data analysis, sparse model selection becomes inevitable once the number of predictor variables is very high. It is well-known that model selection procedures like the Lasso or Boosting tend to overfit on real data. The celebrated Stability Selection overcomes these weaknesses by aggregating models, based on subsamples of the training data, followed by choosing a stable predictor set which is usually much sparser than the predictor sets from the raw models. The standard Stability Selection is based on a global criterion, namely the per-family error rate, while additionally requiring expert knowledge to suitably configure the hyperparameters. Model selection depends on the loss function, i.e., predictor sets selected w.r.t. some particular loss function differ from those selected w.r.t. some other loss function. Therefore, we propose a Stability Selection variant which respects the chosen loss function via an additional validation step based on out-of-sample validation data, optionally enhanced with an exhaustive search strategy. Our Stability Selection variants are widely applicable and user-friendly. Moreover, our Stability Selection variants can avoid the issue of severe underfitting, which affects the original Stability Selection for noisy high-dimensional data, so our priority is not to avoid false positives at all costs but to result in a sparse stable model with which one can make predictions. Experiments where we consider both regression and binary classification with Boosting as model selection algorithm reveal a significant precision improvement compared to raw Boosting models while not suffering from any of the mentioned issues of the original Stability Selection.

Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression

Abstract. The prediction of river water temperature is of key importance in the field of environmental science. Water temperature datasets for low-order rivers are often in short supply, leaving environmental modelers with the challenge of extracting as much information as possible from existing datasets. Therefore, identifying a suitable modeling solution for the prediction of river water temperature with a large scarcity of forcing datasets is of great importance. In this study, five models, forced with the meteorological datasets obtained from the fifth-generation atmospheric reanalysis, ERA5-Land, are used to predict the water temperature of 83 rivers (with 98 % missing data): three machine learning algorithms (random forest, artificial neural network and support vector regression), the hybrid Air2stream model with all available parameterizations and a multiple regression. The machine learning hyperparameters were optimized with a tree-structured Parzen estimator, and an oversampling–undersampling technique was used to generate synthetic training datasets. In general terms, the results of the study demonstrate the vital importance of hyperparameter optimization and suggest that, from a practical modeling perspective, when the number of predictor variables and observed river water temperature values are limited, the application of all the models considered in this study is crucial. Basically, all the models tested proved to be the best for at least one station. The root mean square error (RMSE) and the Nash–Sutcliffe efficiency (NSE) values obtained for the ensemble of all model results were 2.75±1.00 and 0.56±0.48 ∘C, respectively. The model that performed the best overall was random forest (annual mean – RMSE: 3.18±1.06 ∘C; NSE: 0.52±0.23). With the application of the oversampling–undersampling technique, the RMSE values obtained with the random forest model were reduced from 0.00 % to 21.89 % (μ=8.57 %; σ=8.21 %) and the NSE values increased from 1.1 % to 217.0 % (μ=40 %; σ=63 %). These results suggest that the solution proposed has the potential to significantly improve the modeling of water temperature in rivers with machine learning methods, as well as providing increased scope for its application to larger training datasets and the prediction of other types of dependent variables. The results also revealed the existence of a logarithmic correlation among the RMSE between the observed and predicted river water temperature and the watershed time of concentration. The RMSE increases by an average of 0.1 ∘C with a 1 h increase in the watershed time of concentration (watershed area: μ=106 km2; σ=153).

Artificial Intelligence and Machine Learning Applications in Spine Surgery.

The complexity of patients with spine pathology and high rates of complications has driven extensive research directed toward optimizing outcomes and reducing complications. Traditional statistical analysis has been limited both in validity and in the number of predictor variables considered. Over the past decade, artificial intelligence and machine learning have taken center stage as the possible solution to creating more accurate and applicable patient-centered predictive models in spine surgery. This review discusses the current published machine learning applications on preoperative optimization, risk stratification, and predictive modeling for the cervical, lumbar, and adult spinal deformity populations.

On the efficiency of growth forecasts for Germany: an application of forward and backward predictor variable selection

ABSTRACT I use a forward and backward predictor selection to study the efficiency of growth forecasts for Germany published by three German economic research institutes during the sample period 1970–2018. The two predictor selection techniques make it straightforward to study forecast efficiency even when the number of predictor variables that a researcher uses to proxy the information set of a forecaster is large. Based on my empirical results, I reject forecast efficiency in the majority of cases.

Performance of HIV Infection Prediction Models in Men Who Have Sex with Men: A Systematic Review and Meta-Analysis.

Effective ways to identify and predict men who have sex with men (MSM) at substantial risk for HIV is a global priority. HIV risk assessment tools can improve individual risk awareness and subsequent health-seeking actions. We sought to identify and characterize the performance of HIV infection risk prediction models in MSM through systematic review and meta-analysis. PubMed, Embase, and The Cochrane Library were searched. Eighteen HIV infection risk assessment models with a total of 151,422 participants and 3643 HIV cases were identified, eight of which have been externally validated by at least one study (HIRI-MSM, Menza Score, SDET Score, Li Model, DHRS, Amsterdam Score, SexPro model, and UMRSS). The number of predictor variables in each model ranged from three to 12, age, the number of male sexual partners, unprotected receptive anal intercourse, recreational drug usage (amphetamines, poppers), and sexually transmitted infections were critical scoring variables. All eight externally validated models performed well in terms of discrimination, with the pooled area under the receiver operating characteristic curve (AUC) ranging from 0.62 (95%CI: 0.51 to 0.73, SDET Score) to 0.83 (95%CI: 0.48 to 0.99, Amsterdam Score). Calibration performance was only reported in 10 studies (35.7%, 10/28). The HIV infection risk prediction models showed moderate-to-good discrimination performance. Validation of prediction models across different geographic and ethnic environments is needed to ensure their real-world application.

Predictive capacity of nine algorithms and an ensemble model to determine the geographic distribution of tree species

The different models that predict the distribution of species are a useful tool for the evaluation and monitoring of forest resources as they facilitate the planning of their management in a changing climate environment. Recently, a significant number of algorithms have been proposed for this purpose, making it difficult to select the most appropriate to use. The evaluation of performance and predictive stability of these models can elucidate this problem. Distribution data of 17 pine species with high economic importance for Mexico were collected and distribution models were carried out. We carried out a pre-modeling design to select the prediction variables (climatic, edaphic and topographic), after which nine algorithms and an ensemble model were contrasted against one another. The true skill statistic (TSS) and the area under the curve (AUC) were used to evaluate the predictive performance of the models, and the coefficient of variation of the predictions was used to evaluate their stability. The number of predictive variables in the final models fluctuated from 6 to 12; the mean diurnal range and the maximum temperature of warmest month were included in the models for most species. Random forests, the ensemble model, generalized additive models and MaxEnt were the ones that best described the distribution of the species (AUC >0.92 and TSS >0.72); the opposite was found in Bioclim and Domain (AUC<0.75 and <0.82; and TSS<0.5 and <0.55). Support vector machine, Mahalanobis distance, generalized linear models and boosted regression trees obtained intermediate settings. The coefficient of variation indicated that Bioclim, Domain and Support vector machine have low predictive stability (CV>0.055); on the contrary, Maxent and the ensemble model attained high predictive stability (CV<0.015). The ensemble model obtained greater performance and predictive stability in the predictions of the distribution of the 17 species of pines. The differences found in performance and predictive stability of the algorithms suggest that the ensemble model has the potential to model the distribution of tree species.

Modeling MODIS LST at European scale

Background To harmonize exposure assessment of temperature in multi-cohort studies, models require to cover large temporal and geographical extends at high resolution. The daily 1km Land Surface Temperature (LST) product from MODIS instrument is an important satellite predictor in ambient temperature models. However, LST products present an uneven data distribution due to cloud and snow cover. Interpolating LST &amp;#x22;gaps” can become computationally expensive and require performance indicators to evaluate spatial and temporal stability and overfitting. Traditional (global) performance indicators might not be sensitive to changes in performance resulting from local data density and autocorrelation structures. This study aims (1) to evaluate different model configurations to understand how LST spatial and temporal data density patterns affect model performance at global and local levels, and (2) to identify the most stable models. Methods: We aggregated observations into 100km² spatial blocks, trained random forest models and evaluated performance through 5-fold block cross-validation. We tested different model configurations based on the number of predictor variables (latitude, longitude, elevation, date, NDVI &amp;#x26; climatic variables), the model’s temporal extent and the number of sampled instances per block for Europe. Results: Adding predictor variables and increasing the temporal extent dramatically increased time, but not performance. Sampling 25% of the maximum amount of observations per block show the largest drops in RMSE, while keeping the computational times low. We showed some evidence of overfitting when training large extents and sample sizes due to limited representativeness at blocks with less instances. Our best model showed a CV-RMSE of 2.4 Kelvin with 90% of mean RMSE at the block level below 0.93 Kelvin. Conclusions: Using a block sampling approach can balance sample size, thus limiting overfitting and computational time. Keywords: Europe wide, temperature modelling, block cross-validation, Land surface temperature

Vilka faktorer predicerar behärskningen av svenskans preteritumböjning? Om finska gymnasieelevers inlärning av femton vardagliga verb

In this study, we examine how Finnish upper secondary students learning Swedish as a second language inflect Swedish verbs in the past tense. We analyze the answers by 351 students to an online quiz regarding the inflection of 15 common verbs using mixed effects logistic regression modeling. Our results indicate that the correctness of the answers in the verb inflection task can be predicted based on a combination of linguistic and extralinguistic variables. Specifically, with respect to linguistic variables, verb frequency and vowel shift (ablaut) are significant predictors of the correctness of the answers, whereas the traditional distinction between strong and weak verbs is not. With respect to extralinguistic variables, the correctness of the answers is significantly predicted by the students’ gender, age/exposure and level of motivation. Furthermore, we demonstrate through error analysis that, initially, many students follow the same pattern for past tense formation; the verbs’ infinitive form is interpreted as their stem and the past tense suffix -de is overgeneralized to most verbs. On a methodological level, we argue that multivariate analyses allow for a more detailed description of the acquisition order of language structures than what has been achieved by less sophisticated quantitative analyses in prior research, and we encourage other scholars to build on our work by expanding it both in terms of the number of verbs as well as the number of predictor variables.

Predicting peritoneal carcinomatosis of gastric cancer: A simple model to exempt low-risk patients from unnecessary staging laparoscopy.

BackgroundPeritoneal carcinomatosis (PC) of gastric cancer indicates a poor outcome and is mainly diagnosed by staging laparoscopy (SL). This study was designed to develop a risk stratification model based on the number of risk factors to exempt low-risk patients from unnecessary SL.MethodsThis was a retrospective cohort study based on a single institution between January 2015 and December 2019. SL is indicated for patients of advanced locoregional stage, and clinicopathologic characteristics of 535 consecutive patients were included. PC-associated variables were identified by logistic regression analysis. A risk stratification model based on the number of risk factors was constructed, and we defined its predictive value with a receiver operating characteristic (ROC) curve and negative predictive value.ResultsIn total, 15.9% of included patients were found to have PC during SL. Borrmann type IV, elevated CA125, and tumour diameter ≥5 cm were independent risk factors of PC. These three factors combined with cT4 were selected as predictive factors, and the number of predictive variables was significantly related to the possibility of PC (2.0%, 12.8%, 20.0%, 54.2%, and 100%, respectively). When the cutoff value is more than one predictive factor, the negative predictive value is 98.0%, with an area under the curve of 0.780. This model could exempt 29.8% of unnecessary SL compared to the indication of the current NCCN guideline.ConclusionsWe constructed a simple model to predict the probability of PC using the number of predictive factors. It is recommended that patients without any of these factors should be exempt from SL.

Responsible companion animal guardianship is associated with canine visceral leishmaniasis: an analytical cross-sectional survey in an urban area of southeastern Brazil

BackgroundResponsible companion animal guardianship (RCAG) comprises a set of concepts involving activities, behavior and care that guardians must provide to ensure the welfare of their animals. When such principles are disregarded, the risk of animals developing zoonotic diseases, such as canine visceral leishmaniasis (CVL), increases. This disease is a public health problem in many urban settings in Brazil because dogs are the main reservoirs of Leishmania and are involved in the transmission of the parasites to humans. Our analytical cross-sectional epidemiological survey aimed to investigate the prevalence of CVL in a city in southeastern Brazil and to establish the association between the disease and a number of predictor variables including dog traits, socioeconomic status of guardians, ecological features of the domicile and RCAG.ResultsOur study showed that the global prevalence of CVL in the sample canine population was 6.7% (47/704). All variables related to better dog care were associated with lower chances of infection. Multiple regression analysis revealed that the chances of animals being seropositive for CVL were significantly (p < 0.05) higher when guardians had no formal education or possessed a university degree (vs. those with complete primary or secondary schooling) and when dogs were sheltered outside the house and had free access to the streets. An additional novel finding was that dogs that were acquired as puppies presented half of the chance of developing the disease in comparison with those acquired at the adult stage. Geographically weighted logistic regression coefficients showed that the strengths of the predictor/CVL associations varied depending on the studied geographical space. Both models demonstrated that the associations were always in the same directions.ConclusionsOur findings indicate that regardless of age and mode of acquisition, adult dogs should be submitted to clinical evaluation and tests for CVL. RCAG can exert positive effects on the control of CVL.

Analysis of Factors Affecting The Timeliness of Financial Reporting on Manufacturing Companies in The Indonesia Stock Exchange

This study aims to find empirical evidence about the factors that affect the timeliness of financial reporting of manufacturing companies listed on the Indonesia Stock Exchange. The research uses quantitative research methods. The data collected in this study will be analyzed using two methods, namely 1) Descriptive statistical method, the analytical tool used is the average (mean) and standard deviation 2) Hypothesis Testing, hypothesis testing is done by using multivariate test using logistic regression. The sample from this study used 43 manufacturing companies x in 3 years of research, 129 data on financial statements that were consistently listed on the Indonesia Stock Exchange for the period 2014-2016 were taken using the purposive sampling method. Based on Simultaneous Effect Analysis, it can be seen that α= 0.05 and degrees of freedom (df) = k = 5, where k is the number of predictor variables, the value of χ²(p) from the chi-square distribution table is 11.07048. Due to 6.266 &lt; 11.07048 or -2(L0–L1) &lt; χ²(p), it can be said that together (simultaneously), the five predictor variables (DER, ROA, SIZE, KAP, AUDCH) have no effect on the variable of timeliness of financial reporting.

Comparing conventional statistical models and machine learning in a small cohort of South African cardiac patients

BackgroundMachine learning is used to process big data volumes with complex non-linear relationships between predictive variables and predictions. Research into the usefulness of machine learning in small data volumes remains limited. AimTo compare conventional statistical methods and machine learning to predict angiogram outcomes in a small cohort of South African cardiac patients. MethodsThis is a retrospective study on patients with cardiac risk factors at Inkosi Albert Luthuli Central Hospital, Durban, South Africa, from 2002 to 2008. Models were designed using predictive risk factors to forecast a binary angiogram outcome (normal or abnormal) by applying conventional statistical models (binary logistic and log binomial) and stacking ensemble machine learning. ResultsThe outcome prevalence of abnormal angiograms was 99/173 (57%). Predictive data was used to model this outcome. The binary logistic regression model, which estimates odds ratio, was unsuitable. The log binomial model, which estimates relative risk, did not converge after various stepwise modelling attempts. Thereafter, machine learning models were used. These included logistic regression, k-nearest neighbour, decision tree, support vector machine, and naïve Bayes. The ensemble model amalgamated all algorithms and showed accuracy >70% and excellent performance at different thresholds with an area under the curve (AUC) > 80%. DiscussionThe logistic regression model was unsuitable because an odds ratio would have been unreliable and overestimated the true effect since the outcome prevalence was >10%. A log binomial model with relative risk estimates did not converge, possibly owing to the multiple predictive variables. Overall, conventional statistical models were unsuccessful in this instance. Machine learning models had limitations from a small dataset. However, the combined modelling with the stacking ensemble method produced good results in the small, homogenous database by exploiting the strengths of each contributing algorithm. ConclusionsResearchers may apply machine learning when conventional statistical models are inconclusive in homogenous small databases with multiple variables and a complex relationship to the outcome. Machine learning is a viable option even with relatively small cohorts if the number of predictive variables is also small.

Preoperative predictors for a successful return to sport following anterior cruciate ligament reconstruction (ACLR): a protocol for a systematic review and meta-analysis

IntroductionReturn to sport (RTS) following anterior cruciate ligament reconstruction (ACLR) is the primary goal for most patients. However, the decision of when to RTS is difficult for patients and clinicians...

A comparison of two dissimilarity functions for mixed-type predictor variables in the delta -machine

The delta -machine is a statistical learning tool for classification based on dissimilarities or distances between profiles of the observations to profiles of a representation set, which was proposed by Yuan et al. (J Claasif 36(3): 442–470, 2019). So far, the delta -machine was restricted to continuous predictor variables only. In this article, we extend the delta -machine to handle continuous, ordinal, nominal, and binary predictor variables. We utilized a tailored dissimilarity function for mixed type variables which was defined by Gower. This measure has properties of a Manhattan distance. We develop, in a similar vein, a Euclidean dissimilarity function for mixed type variables. In simulation studies we compare the performance of the two dissimilarity functions and we compare the predictive performance of the delta -machine to logistic regression models. We generated data according to two population distributions where the type of predictor variables, the distribution of categorical variables, and the number of predictor variables was varied. The performance of the delta -machine using the two dissimilarity functions and different types of representation set was investigated. The simulation studies showed that the adjusted Euclidean dissimilarity function performed better than the adjusted Gower dissimilarity function; that the delta -machine outperformed logistic regression; and that for constructing the representation set, K-medoids clustering achieved fewer active exemplars than the one using K-means clustering while maintaining the accuracy. We also applied the delta -machine to an empirical example, discussed its interpretation in detail, and compared the classification performance with five other classification methods. The results showed that the delta -machine has a good balance between accuracy and interpretability.

High-dimensional linear discriminant analysis with moderately clipped LASSO

There is a direct connection between linear discriminant analysis (LDA) and linear regression since the direction vector of the LDA can be obtained by the least square estimation. The connection motivates the penalized LDA when the model is high-dimensional where the number of predictive variables is larger than the sample size. In this paper, we study the penalized LDA for a class of penalties, called the moderately clipped LASSO (MCL), which interpolates between the least absolute shrinkage and selection operator (LASSO) and minimax concave penalty. We prove that the MCL penalized LDA correctly identifies the sparsity of the Bayes direction vector with probability tending to one, which is supported by better finite sample performance than LASSO based on concrete numerical studies.

Predicting Quality and Yield of Growing Alfalfa from a UAV

HighlightsThe canopy height distributions from photogrammetry were mostly Gaussian distributions.The mean canopy height and standard deviation from photogrammetry can be used to predict yield and nutritive values.Including average field maturity and pest pressures improves the models to R2 values of around 0.8.The best predictive model types were generally Gaussian random processes.Abstract. Alfalfa producers would be able to manage their crop production practices better if they knew the distribution of yield and nutritive values of the alfalfa growing throughout their fields. Unmanned aerial vehicles (UAVs) equipped with cameras and photogrammetry techniques provide methods to quickly capture the plant canopy structure at field scale. The goal of this study was to determine how to use the point clouds produced by the photogrammetry process to estimate the yield and nutritive value of alfalfa throughout its growth cycle. During the 2017 growing season, weekly measurements were taken of 1 m2 quadrats (~20 per week, 325 total) in a field of alfalfa managed for forage production. Measurements in each quadrat included manual measurements of maximum and average height, weed presence, disease damage, insect damage, maturity level, stand plant density, and many images of the quadrat from a UAV. After processing to remove outliers, the canopy heights from the photogrammetry point clouds were largely Gaussian distributions. Models were developed using supervised machine learning to estimate yield and nutritive values, including acid detergent fiber (ADF), neutral detergent fiber (NDF), and crude protein (CP), with different numbers of predictor (input) variables. Simple models with two predictor variables were only based on the mean and standard deviation of the heights of the photogrammetry point cloud. The models with three predictor variables added average field maturity level. Finally, the models with six predictor variables added weed presence, insect damage, and disease damage. A linear regression with all interaction terms was found to be the best type of model for predicting yield with six variables. For all other outputs and numbers of predictor variables, a Gaussian random process (GRP) model was best. The models improved with additional predictor variables, so the six-variable models were best able to predict yield and nutritive value. The R2 values for the six-variable models for predicting yield, ADF, NDF, and CP were 0.81, 0.81, 0.78, and 0.79, respectively. Keywords: Alfalfa, Machine learning, Nutritive value, Photogrammetry, Unmanned aerial vehicle, Yield.

Gene selection and classification of microarray gene expression data based on a new adaptive L1-norm elastic net penalty

The removal of irrelevant and insignificant genes has always been a major step in microarray data analysis. The application of gene selection methods in biological datasets has greatly increased, supporting expert systems in cancer diagnostic capability with high classification accuracy. Penalized logistic regression (PLR) using the elastic net (EN) has been widely used in high-dimensional cancer classification in recent years to estimate the gene coefficients and perform gene selection simultaneously. However, the EN estimator does not satisfy the oracle properties. This paper proposes the PLR using the adaptive elastic net (AEN), abbreviated as PLRAEN, to address the inconsistency. Our method employs the ratio (BWR) as an initial weight inside the L1-norm of the EN model. Several experiments were performed on a simulation study for a different number of predictor variables, sample sizes, and correlation coefficients and also on three public gene expression datasets to evaluate the effectiveness. Experimental results demonstrate that the proposed method consistently outperforms two other contemporary penalized methods regarding classification accuracy and the number of selected genes. Therefore, we conclude that PLRAEN is a better method to implement gene selection in the high-dimensional cancer classification field.

Participation predictors for leisure-time physical activity intervention in children with cerebral palsy.

To determine the predictors of magnitude of change in response to a participation-focused leisure-time physical activity intervention in children with cerebral palsy (CP) using the ParticiPAte CP protocol. We included 33 children (16 males, 17 females) aged 8 to 12years (mean age=10y, SD=1y 6mo) with CP with pre/postintervention data from a wait-list randomized trial. The hypothesized linear predictors of change in primary outcomes (Canadian Occupational Performance Measure [COPM]-performance and COPM-satisfaction, Belief in Goal Self-Competence Scale (BiGSS), and minutes per day moderate-to-vigorous physical activity [MVPA]) were: age; Gross Motor Function Classification System level; comorbid autism spectrum disorder (ASD); Goal Attainment Scaling T score; Problems in Schools Questionnaire; Physical Activity Climate Questionnaire; Motives for Physical Activities Measure-Revised; and stage of behaviour change. Multivariable models were selected using the Bayesian information criterion. Overcoming barriers to participation, age, and comorbid ASD explained 49% of the variance in change in COPM-performance. Being motivated by interest and/or enjoyment and age explained 32% of the variance in change in COPM-satisfaction. Being motivated by physical activity competence or appearance (extrinsic motivation) explained 24% of the variance in change in BiGSS. Parental autonomy supportiveness, overcoming barriers to participation, appearance motivation, and baseline MVPA explained 59% of the variance in change in MVPA. These findings support a behaviour paradigm for conceptualizing physical activity in children with CP. Children who met their treatment goals showed a greater increase in physical activity participation. Children who were more intrinsically motivated by physical activity at baseline improved more. Being older and having a comorbid diagnosis of autism spectrum disorder were associated with an attenuated effect of the therapy.

Generalized Linear Mixed Model Analysis of Acute Respiratory Infection Data on Children

Statistical modeling often involves data which has a distribution of the exponential family. Generalized Linear Model (GLM) was developed to model these data by using a link function between the mean of the response variable and the linear form of the predictor variable. If the data of the response variable comes from several census blocks that are taken randomly, then the diversity between census blocks should not be ignored because it can increase bias. The Generalized Linear Mixed Model (GLMM) is a method that can capture a variety of random effects. However, it does not rule out if there are many predictor variables involved in the model and we use GLMMLasso as a combination method of GLMM and Lasso to shrink the parameter coefficients to zero, it is used to reduce the variance. In this study, a simulation was conducted to GLMMLasso use different numbers of predictor variables and different values of shrinkage coefficients to determine which shrinkage coefficient values have a minimum bias on parameter prediction. Acute Respiratory Infection (API) data on children in Jakarta is used to know the factors that could cause increased cases. The simulation result is the shrinkage coefficient which produces a minimum bias is 30, and the R2 value of data analysis on the model is 99.24%