Two-step robust estimator in heteroscedastic regression model in the presence of outliers

Although the ordinary least squares (OLS) estimates are unbiased in the presence of heteroscedasticity, these are no longer efficient. This problem becomes more complicated when the violation of constant error variances comes together with the existence of outliers. The weighted least squares (WLS) procedure is often used to estimate the regression parameters when heteroscedasticity occurs in the data. But there is evidence that the WLS estimators suffer a huge set back in the presence of outliers. Moreover, the use of the WLS requires a known form of the heteroscedastic errors structures. To rectify this problem, we proposed a new method that we call two step robust weighted least squares (TSRWLS) method where prior information on the structure of the heteroscedastic errors is not required. In the proposed procedure, the robust technique is used twice. Firstly, the robust weights are used for solving the heteroscedasic error and secondly, the robust weighting function is used for eliminating the effect of outliers. The performance of the newly proposed estimator is investigated extensively by real data sets and Monte Carlo simulations

Text to image synthesis for improved image captioning

Generating textual descriptions of images has been an important topic in computer vision and natural language processing. A number of techniques based on deep learning have been proposed on this topic. These techniques use human-annotated images for training and testing the models. These models require a large number of training data to perform at their full potential. Collecting human generated images with associative captions is expensive and time-consuming. In this paper, we propose an image captioning method that uses both real and synthetic data for training and testing the model. We use a Generative Adversarial Network (GAN) based text to image generator to generate synthetic images. We use an attention-based image captioning method trained on both real and synthetic images to generate the captions. We demonstrate the results of our models using both qualitative and quantitative analysis on popularly used evaluation metrics. We show that our experimental results achieve two fold benefits of our proposed work: i) it demonstrates the effectiveness of image captioning for synthetic images, and ii) it further improves the quality of the generated captions for real images, understandably because we use additional images for training

RamanNet: A generalized neural network architecture for Raman Spectrum Analysis

Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kind of materials. This sort of fingerprinting molecules has thus led to widespread application of Raman spectrum in various fields like medical dignostics, forensics, mineralogy, bacteriology and virology etc. Despite the recent rise in Raman spectra data volume, there has not been any significant effort in developing generalized machine learning methods for Raman spectra analysis. We examine, experiment and evaluate existing methods and conjecture that neither current sequential models nor traditional machine learning models are satisfactorily sufficient to analyze Raman spectra. Both has their perks and pitfalls, therefore we attempt to mix the best of both worlds and propose a novel network architecture RamanNet. RamanNet is immune to invariance property in CNN and at the same time better than traditional machine learning models for the inclusion of sparse connectivity. Our experiments on 4 public datasets demonstrate superior performance over the much complex state-of-the-art methods and thus RamanNet has the potential to become the defacto standard in Raman spectra data analysi

Implications of ecotourism development in protected areas: A study from Rema-Kalenga Wildlife Sanctuary, Bangladesh

This article is based on visitors profile study of protected area based tourist spots of Rema-Kalenga Wildlife Sanctuary (RKWS), Bangladesh to ascertain the potential of ecotourism. Study findings shows that 69% male constitute the visitors group while the maximum number of visitors was found in the age of below 30 years. Most of the visitors were literate and among them 43% visitors were student. Most (53%) of visitors preferred to get recreation in holidays as they were employed. Visitors were highly preferred to come with friends group. About 92% respondents showed positive mind to come here in future while 89% respondents view that park has tourism potential. Most of the respondents reported the presence of wildlife (48%) most notable followed by plant diversity and tribal community as recreational. From χ2 test it is found that highly significance association present between tourism potentiality of the wildlife sanctuary and some demographic variable like income of tourists (χ2 = 49.138, p < 0.000), visiting pattern (χ2 = 19.344, p < 0.000), education of tourists (χ2 = 50.226, p < 0.000), travelling distance (Km - χ2 = 11.427, p < 0.022), duration of staying (χ2 = 12.867, p < 0.002), frequency of visit (χ2 = 8.456, p < 0.015), visiting time (χ2 = 6.530, p < 0.011), problem in the study area (χ2 = 14.962, p < 0.021), occupation of tourists (χ2 = 8.848, p < 0.031). If the problems addressed by the visitors were solved, RKWS would be a bright place of eco-tourism in Bangladesh

Robust Wild Bootstrap for Stabilizing the Variance of Parameter Estimates in Heteroscedastic Regression Models in the Presence of Outliers

Nowadays bootstrap techniques are used for data analysis in many other fields like engineering, physics, meteorology, medicine, biology, and chemistry. In this paper, the robustness of Wu (1986) and Liu (1988)'s Wild Bootstrap techniques is examined. The empirical evidences indicate that these techniques yield efficient estimates in the presence of heteroscedasticity problem. However, in the presence of outliers, these estimates are no longer efficient. To remedy this problem, we propose a Robust Wild Bootstrap for stabilizing the variance of the regression estimates where heteroscedasticity and outliers occur at the same time. The proposed method is based on the weighted residuals which incorporate the MM estimator, robust location and scale, and the bootstrap sampling scheme of Wu (1986) and Liu (1988). The results of this study show that the proposed method outperforms the existing ones in every respect

Functional disability and social participation restriction associated with chronic conditions in middle-aged and older adults

Abstract : Background. We examine the population impact on functional disability and social participation of physical and mental chronic conditions individually and in combination. Methods. Cross-sectional, population-based data from community-dwelling people aged 45 years and over living in the 10 Canadian provinces in 2008–2009 were used to estimate the population attributable risk (PAR) for functional disability in basic (ADL) and instrumental (IADL) activities of daily living and social participation restrictions for individual and combinations of chronic conditions, stratified by age and gender, after adjusting for confounding variables. Results. Five chronic conditions (arthritis, depression, diabetes, heart disease and eye disease) made the largest contributions to ADL-related and IADL-related functional disability and social participation restrictions, with variation in magnitude and ranking by age and gender. While arthritis was consistently associated with higher PARs across gender and most age groups, depression, alone and in combination with the physical chronic conditions, was associated with ADL and IADL disability as well as social participation restrictions in the younger age groups, especially among women. Compared to women, the combinations of conditions associated with higher PARs in men more often included heart disease and diabetes. Conclusions. Our findings suggest that in community dwelling middle-aged and older adults, the impact of combinations of mental and physical chronic conditions on functional disability and social participation restriction is substantial and differed by gender and age. Recognising the differences in the drivers of PAR by gender and age group will ultimately increase the efficiency of clinical and public health interventions

A robust modification of the Goldfeld-Quandt Test for the detection of heteroscedasticity in the presence of outliers

Problem statement: The problem of heteroscedasticity occurs in regression analysis for many practical reasons. It is now evident that the heteroscedastic problem affects both the estimation and test procedure of regression analysis, so it is really important to be able to detect this problem for possible remedy. The existence of a few extreme or unusual observations that we often call outliers is a very common feature in data analysis. In this study we have shown how the existence of outliers makes the detection of heteroscedasticity cumbersome. Often outliers occurring in a homoscedastic model make the model heteroscedastic, on the other hand, outliers may distort the diagnostic tools in such a way that we cannot correctly diagnose the heteroscedastic problem in the presence of outliers. Neither of these situations is desirable. Approach: This article introduced a robust test procedure to detect the problem of heteroscedasticity which will be unaffected in the presence of outliers. We have modified one of the most popular and commonly used tests, the Goldfeld-Quandt, by replacing its nonrobust components by robust alternatives. Results: The performance of the newly proposed test is investigated extensively by real data sets and Monte Carlo simulations. The results suggest that the robust version of this test offers substantial improvements over the existing tests. Conclusion/Recommendations: The proposed robust Goldfeld-Quandt test should be employed instead of the existing tests in order to avoid misleading conclusion

A robust rescaled moment test for normality in regression

Problem statement: Most of the statistical procedures heavily depend on normality assumption of observations. In regression, we assumed that the random disturbances were normally distributed. Since the disturbances were unobserved, normality tests were done on regression residuals. But it is now evident that normality tests on residuals suffer from superimposed normality and often possess very poor power. Approach: This study showed that normality tests suffer huge set back in the presence of outliers. We proposed a new robust omnibus test based on rescaled moments and coefficients of skewness and kurtosis of residuals that we call robust rescaled moment test. Results: Numericalexamples and Monte Carlo simulations showed that this proposed test performs better than the existingtests for normality in the presence of outliers. Conclusion/Recommendation: We recommend using ourproposed omnibus test instead of the existing tests for checking the normality of the regressionresiduals

The performance of robust weighted least squares in the presence of outliers and heteroscedastic errors

The Ordinary Least Squares (OLS) method is the most popular technique in statistics and is often use to estimate the parameters of a model because of tradition and ease of computation. The OLS provides an efficient and unbiased estimates of the parameters when the underlying assumptions, especially the assumption of contant error variances (homoscedasticity), are satisfied. Nonetheless, in real situation it is difficult to retain the error variance homogeneous for many practical reasons and thus there arises the problem of heteroscedasticity. We generally apply the Weighted Least Squares (WLS) procedure to estimate the regression parameters when heteroscedasticity occurs in the data. Nevertheless, there is evidence that the WLS estimators suffer a huge set back in the presence of a few atypical observations that we often call outliers. In this situation the analysis will become more complicated. In this paper we have proposed a robust procedure for the estimation of regression parameters in the situation where heteroscedasticity comes together with the existence of outliers. Here we have employed robust techniques twice, once in estimating the group variances and again in determining weights for the least squares. We call this method Robust Weighted Least Squares (RWLS). The performance of the newly proposed method is investigated extensively by real data sets and Monte Carlo Simulations. The results suggest that the RWLS method offers substantial improvements over the existing methods

Ophthalmic Biomarker Detection Using Ensembled Vision Transformers -- Winning Solution to IEEE SPS VIP Cup 2023

This report outlines our approach in the IEEE SPS VIP Cup 2023: Ophthalmic Biomarker Detection competition. Our primary objective in this competition was to identify biomarkers from Optical Coherence Tomography (OCT) images obtained from a diverse range of patients. Using robust augmentations and 5-fold cross-validation, we trained two vision transformer-based models: MaxViT and EVA-02, and ensembled them at inference time. We find MaxViT's use of convolution layers followed by strided attention to be better suited for the detection of local features while EVA-02's use of normal attention mechanism and knowledge distillation is better for detecting global features. Ours was the best-performing solution in the competition, achieving a patient-wise F1 score of 0.814 in the first phase and 0.8527 in the second and final phase of VIP Cup 2023, scoring 3.8% higher than the next-best solution