Prediction and Analysis of Housing Price Based on the Generalized Linear Regression Model

Li, Xinshu

doi:10.1155/2022/3590224

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…An SVM module based on the "e1071" package was created to further evaluate the diagnostic value of the selected biomarker in GC (30). GLM could dynamically assess the association between normally distributed dependent traits and categorical or continuous independent traits (31). XGB is a collection of boosted trees based on gradient boosting, thus making detailed comparability between classification error and model sophistication (32).…”

Section: Establishment Of the Prediction Model Based On Machine Learn...mentioning

confidence: 99%

Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer

Qing,

Jiang,

Yuan

et al. 2023

Front. Endocrinol.

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BackgroundAccumulative studies have demonstrated the close relationship between tumor immunity and pyroptosis, apoptosis, and necroptosis. However, the role of PANoptosis in gastric cancer (GC) is yet to be fully understood.MethodsThis research attempted to identify the expression patterns of PANoptosis regulators and the immune landscape in GC by integrating the GSE54129 and GSE65801 datasets. We analyzed GC specimens and established molecular clusters associated with PANoptosis-related genes (PRGs) and corresponding immune characteristics. The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. Additionally, this study discussed the relationship between infiltrating immune cells and variables in the selected model.ResultsThis study identified dysregulated PRGs and differential immune activities between GC and normal samples, and further identified two PANoptosis-related molecular clusters in GC. These clusters demonstrated remarkable immunological heterogeneity, with Cluster1 exhibiting abundant immune infiltration. The Support Vector Machine signature was found to have the best discriminative ability, and a 5-gene-based SVM signature was established. This model showed excellent performance in the external validation cohorts, and the nomogram, calibration curve, and DCA indicated its reliability in predicting GC patterns. Further analysis confirmed that the 5 selected variables were remarkably related to infiltrating immune cells and immune-related pathways.ConclusionTaken together, this work demonstrates that the PANoptosis pattern has the potential as a stratification tool for patient risk assessment and a reflection of the immune microenvironment in GC.

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Section: Establishment Of the Prediction Model Based On Machine Learn...mentioning

confidence: 99%

Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer

Qing,

Jiang,

Yuan

et al. 2023

Front. Endocrinol.

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“…Under this circumstance, some factors related to supply and demand will influence the price of real estate. Specifically, there are several kinds of demands, such as self-use demand, investment demand and speculative [3]. Liu found the main factor influencing the price of real estate is the income of residents by using multiple linear regression [4].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Factors Influencing Housing Prices in Yunnan and Guizhou Based on Linear Regression

Fang

2024

HBEM

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The real estate industry contributes significantly to the national economy. In reality, it has a significant impact on the lives of residents. It is of great significance to study the factors that affect the housing price for the development of the real estate industry. Based on the real estate data of Yunnan Province and Guizhou Province from January 2012 to December 2022, this study will analyze the factors influencing the housing prices of Yunnan Province and Guizhou Province. In addition to SPSS research, multiple regression analysis will be used in this paper. According to the analysis, housing prices in Yunnan and Guizhou provinces will rise with the decline of long-term loan interest rates. Besides, the residential consumer price index negatively affects housing prices in Yunnan and Guizhou provinces. Moreover, an increase in the amount of real estate investment will lead to an increase in housing prices. On this basis, the completed real estate area has a negative relationship with house prices.

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“…In this situation, informed and careful decision-making is essential, and predictive models such as the Autoregressive Integrated Moving Average (ARIMA) model have become a helpful tool for analysts and consumers. The real estate market is affected by many factors, according to government policies in the macroeconomy and local house supply and demand dynamics in the microeconomy [3].…”

Section: Introductionmentioning

confidence: 99%

Chinese Housing Prices Prediction using Autoregressive Integrated Moving Average Model

Wan

2024

HSET

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This paper investigates the application of the Autoregressive Integrated Moving Average (ARIMA) model to predict future trends in Chinese housing prices. The Chinese real estate market, characterized by its volatility, especially during the post-COVID-19 period, presents a complex environment for buyers and investors. The paper investigates how the ARIMA model is employed to make informed predictions in this uncertain market. Although it has some limitations, such as a heavy reliance on historical data and insensitivity to unexpected macroeconomic shifts, the ARIMA model offers a structure for understanding and anticipating housing price trends. The paper integrates various data sources, including long-term housing price statistics, to build a comprehensive ARIMA model tailored to the nuances of China’s housing market. This essay demonstrates the ARIMA model's utility in aiding stakeholders to make more confident and informed decisions in an increasingly complex and unpredictable market. The analysis further suggests refinements to the ARIMA model, considering the multifaceted nature of the housing market, influenced by macroeconomic factors and public perception. The final goal is to enhance the model's accuracy and reliability, making it an indispensable tool in economic and policy decision-making in China's evolving real estate landscape.

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Prediction and Analysis of Housing Price Based on the Generalized Linear Regression Model

Cited by 6 publications

References 9 publications

Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer

Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer

Analysis of the Factors Influencing Housing Prices in Yunnan and Guizhou Based on Linear Regression

Chinese Housing Prices Prediction using Autoregressive Integrated Moving Average Model

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