[et_pb_section fb_built=”1″ custom_padding_last_edited=”off|tablet” admin_label=”Header” _builder_version=”4.24.0″ _module_preset=”default” background_color=”#ff6d29″ background_image=”https:\/\/as.nida.ac.th\/storage\/2024\/01\/venture_02.png” custom_padding=”||0px||false|false” custom_padding_phone=”60px||||false|false” locked=”off” global_colors_info=”{%22gcid-a1027310-177f-434f-a6d4-64bc6bfc2d09%22:%91%22background_color%22%93}”][et_pb_row use_custom_gutter=”on” gutter_width=”4″ _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.0″ _module_preset=”d72c0383-6487-4f2c-ac5c-7d48a6376757″ header_font=”Chakra Petch|700|||||||” header_text_color=”#FFFFFF” header_font_size=”60px” header_line_height=”1.2em” header_font_size_tablet=”50px” header_font_size_phone=”24px” header_font_size_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ _module_preset=”default” custom_padding=”||0px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/as.nida.ac.th\/storage\/2024\/01\/electric-services-38.png” title_text=”electric-services-38″ _builder_version=”4.16″ _module_preset=”default” custom_margin=”|-15%|||false|false” custom_margin_tablet=”|-5%|||false|false” custom_margin_phone=”|-5%|||false|false” custom_margin_last_edited=”on|phone” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Staff” _builder_version=”4.16″ custom_padding=”||0px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.26.0″ custom_margin=”|auto|-41px|auto||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.26.0″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.26.0″ _module_preset=”default” text_text_color=”#fe6d2a” text_font_size=”70px” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n
[\/et_pb_text][et_pb_text _builder_version=”4.26.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/as.nida.ac.th\/storage\/2026\/02\/539949716_1262077699264104_6715374486699383280_n.jpg” title_text=”539949716_1262077699264104_6715374486699383280_n” _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.26.0″ _module_preset=”default” min_height=”565.6px” global_colors_info=”{}”][et_pb_row _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
Mongkol Hunkrajok , Wanrudee Skulpakdee<\/span> Recently, non-monotonic rate sequences of pure birth processes have been the focus of much attention in the analysis of count data due to their ability to provide a combination of over-, under-, and equidispersed distributions without the need to reuse covariates (traditional methods). They also permit the modeling of excess counts, a frequent issue arising when using count models based on monotonic rate sequences such as the Poisson, gamma, Weibull, Conway-Maxwell-Poisson (CMP), Faddy (1997), etc. Matrix-exponential approaches have always been used for computing the probabilities for count models based on pure birth processes, although none have been proposed for them as a specific algorithm. It is intractable to calculate these pure birth probabilities numerically in an analytic form because severe numerical cancellations may occur. However, we circumvent this difficulty by exploiting a Taylor series expansion, and then a new analytic form is derived. We developed a simple algorithm for efficiently implementing the new formula and conducted numerical experiments to study the efficiency and accuracy of the developed algorithm. The results indicate that this new approach is faster and more accurate than the matrix-exponential methods.<\/span><\/p>\n<\/div>\n [\/et_pb_text][et_pb_image src=”https:\/\/as.nida.ac.th\/storage\/2026\/02\/514266770_1262077789264095_1672533909804733123_n.jpg” _builder_version=”4.26.0″ _module_preset=”default” title_text=”514266770_1262077789264095_1672533909804733123_n” hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][et_pb_text _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n \u0e2d\u0e32\u0e19\u0e19\u0e17\u0e4c \u0e2a\u0e38\u0e01\u0e2a\u0e31\u0e01,\u0e40\u0e2d\u0e01\u0e23\u0e31\u0e10 \u0e23\u0e31\u0e10\u0e01\u0e32\u0e0d\u0e08\u0e19\u0e4c \u0e1b\u0e23\u0e30\u0e40\u0e17\u0e28\u0e44\u0e17\u0e22\u0e01\u0e4d\u0e32\u0e25\u0e31\u0e07\u0e40\u0e1c\u0e0a\u0e34\u0e0d\u0e1b\u0e31\u0e0d\u0e2b\u0e32\u0e01\u0e32\u0e23\u0e02\u0e32\u0e14\u0e41\u0e04\u0e25\u0e19\u0e41\u0e25\u0e30\u0e01\u0e32\u0e23\u0e01\u0e23\u0e30\u0e08\u0e32\u0e22\u0e15\u0e31\u0e27\u0e44\u0e21\u0e48\u0e2a\u0e21\u0e14\u0e38\u0e25\u0e02\u0e2d\u0e07\u0e41\u0e1e\u0e17\u0e22\u0e4c\u0e40\u0e27\u0e0a\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c\u0e04\u0e23\u0e2d\u0e1a\u0e04\u0e23\u0e31\u0e27 \u0e0b\u0e36\u0e48\u0e07\u0e40\u0e1b\u0e47\u0e19\u0e1a\u0e38\u0e04\u0e25\u0e32\u0e01\u0e23\u0e2a\u0e4d\u0e32\u0e04\u0e31\u0e0d\u0e43\u0e19\u0e23\u0e30\u0e1a\u0e1a\u0e1a\u0e23\u0e34\u0e01\u0e32\u0e23\u0e2a\u0e38\u0e02\u0e20\u0e32\u0e1e\u0e1b\u0e10\u0e21\u0e20\u0e39\u0e21\u0e34 \u0e17\u0e48\u0e32\u0e21\u0e01\u0e25\u0e32\u0e07\u0e1a\u0e23\u0e34\u0e1a\u0e17\u0e02\u0e2d\u0e07\u0e2a\u0e31\u0e07\u0e04\u0e21\u0e2a\u0e39\u0e07\u0e27\u0e31\u0e22\u0e41\u0e25\u0e30\u0e20\u0e32\u0e23\u0e30\u0e42\u0e23\u0e04\u0e44\u0e21\u0e48\u0e15\u0e34\u0e14\u0e15\u0e48\u0e2d\u0e40\u0e23\u0e37\u0e49\u0e2d\u0e23\u0e31\u0e07 (NCDs) \u0e17\u0e35\u0e48\u0e40\u0e1e\u0e34\u0e48\u0e21\u0e02\u0e36\u0e49\u0e19 \u0e07\u0e32\u0e19\u0e27\u0e34\u0e08\u0e31\u0e22\u0e19\u0e35\u0e49\u0e21\u0e35\u0e27\u0e31\u0e15\u0e16\u0e38\u0e1b\u0e23\u0e30\u0e2a\u0e07\u0e04\u0e4c\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e1e\u0e31\u0e12\u0e19\u0e32\u0e40\u0e04\u0e23\u0e37\u0e48\u0e2d\u0e07\u0e21\u0e37\u0e2d\u0e1e\u0e22\u0e32\u0e01\u0e23\u0e13\u0e4c\u0e04\u0e27\u0e32\u0e21\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e41\u0e1e\u0e17\u0e22\u0e4c\u0e40\u0e27\u0e0a\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c\u0e04\u0e23\u0e2d\u0e1a\u0e04\u0e23\u0e31\u0e27\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e08\u0e31\u0e07\u0e2b\u0e27\u0e31\u0e14 \u0e23\u0e30\u0e2b\u0e27\u0e48\u0e32\u0e07\u0e1b\u0e35 \u0e1e.\u0e28. 2568\u20132578 \u0e42\u0e14\u0e22\u0e1a\u0e39\u0e23\u0e13\u0e32\u0e01\u0e32\u0e23\u0e41\u0e19\u0e27\u0e17\u0e32\u0e07 Workload Indicators of Staffing Need (WISN) \u0e01\u0e31\u0e1a\u0e40\u0e17\u0e04\u0e19\u0e34\u0e04 Machine Learning (ML) \u0e44\u0e14\u0e49\u0e41\u0e01\u0e48 Random Forest,XGBoost, Gaussian Process Regression \u0e41\u0e25\u0e30 Linear Regression \u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e04\u0e32\u0e14\u0e01\u0e32\u0e23\u0e13\u0e4c\u0e41\u0e19\u0e27\u0e42\u0e19\u0e49\u0e21\u0e1a\u0e23\u0e34\u0e01\u0e32\u0e23\u0e2a\u0e38\u0e02\u0e20\u0e32\u0e1e \u0e40\u0e0a\u0e48\u0e19 \u0e08\u0e4d\u0e32\u0e19\u0e27\u0e19\u0e1c\u0e39\u0e49\u0e1b\u0e48\u0e27\u0e22\u0e19\u0e2d\u0e01\u0e41\u0e25\u0e30\u0e1c\u0e39\u0e49\u0e1b\u0e48\u0e27\u0e22 NCDs<\/p>\n \u0e01\u0e32\u0e23\u0e28\u0e36\u0e01\u0e29\u0e32\u0e2d\u0e22\u0e39\u0e48\u0e23\u0e30\u0e2b\u0e27\u0e48\u0e32\u0e07\u0e14\u0e4d\u0e32\u0e40\u0e19\u0e34\u0e19\u0e01\u0e32\u0e23 \u0e42\u0e14\u0e22\u0e43\u0e0a\u0e49\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e17\u0e38\u0e15\u0e34\u0e22\u0e20\u0e39\u0e21\u0e34\u0e08\u0e32\u0e01\u0e2b\u0e25\u0e32\u0e22\u0e41\u0e2b\u0e25\u0e48\u0e07 \u0e40\u0e0a\u0e48\u0e19 \u0e10\u0e32\u0e19\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e02\u0e2d\u0e07\u0e2a\u0e4d\u0e32\u0e19\u0e31\u0e01\u0e07\u0e32\u0e19\u0e2b\u0e25\u0e31\u0e01\u0e1b\u0e23\u0e30\u0e01\u0e31\u0e19\u0e2a\u0e38\u0e02\u0e20\u0e32\u0e1e\u0e41\u0e2b\u0e48\u0e07\u0e0a\u0e32\u0e15\u0e34\u0e2a\u0e4d\u0e32\u0e19\u0e31\u0e01\u0e07\u0e32\u0e19\u0e1b\u0e23\u0e30\u0e01\u0e31\u0e19\u0e2a\u0e31\u0e07\u0e04\u0e21 \u0e01\u0e23\u0e21\u0e1a\u0e31\u0e0d\u0e0a\u0e35\u0e01\u0e25\u0e32\u0e07 \u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e1b\u0e23\u0e30\u0e0a\u0e32\u0e01\u0e23\u0e08\u0e32\u0e01\u0e2a\u0e4d\u0e32\u0e19\u0e31\u0e01\u0e07\u0e32\u0e19\u0e2a\u0e20\u0e32\u0e1e\u0e31\u0e12\u0e19\u0e32\u0e01\u0e32\u0e23\u0e40\u0e28\u0e23\u0e29\u0e10\u0e01\u0e34\u0e08\u0e41\u0e25\u0e30\u0e2a\u0e31\u0e07\u0e04\u0e21\u0e41\u0e2b\u0e48\u0e07\u0e0a\u0e32\u0e15\u0e34\u0e41\u0e25\u0e30\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e20\u0e32\u0e23\u0e30\u0e07\u0e32\u0e19\u0e08\u0e32\u0e01\u0e01\u0e32\u0e23\u0e17\u0e1a\u0e17\u0e27\u0e19\u0e27\u0e23\u0e23\u0e13\u0e01\u0e23\u0e23\u0e21\u0e41\u0e25\u0e30\u0e1c\u0e39\u0e49\u0e40\u0e0a\u0e35\u0e48\u0e22\u0e27\u0e0a\u0e32\u0e0d\u0e23\u0e27\u0e21\u0e16\u0e36\u0e07\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e08\u0e32\u0e01\u0e42\u0e04\u0e23\u0e07\u0e01\u0e32\u0e23\u0e27\u0e34\u0e08\u0e31\u0e22\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e0a\u0e32\u0e15\u0e34\u0e40\u0e01\u0e35\u0e48\u0e22\u0e27\u0e01\u0e31\u0e1a\u0e01\u0e32\u0e23\u0e27\u0e32\u0e07\u0e41\u0e1c\u0e19\u0e01\u0e4d\u0e32\u0e25\u0e31\u0e07\u0e04\u0e19\u0e41\u0e1e\u0e17\u0e22\u0e4c\u0e40\u0e09\u0e1e\u0e32\u0e30\u0e17\u0e32\u0e07\u0e1c\u0e25\u0e01\u0e32\u0e23\u0e27\u0e34\u0e40\u0e04\u0e23\u0e32\u0e30\u0e2b\u0e4c\u0e08\u0e30\u0e16\u0e39\u0e1e\u0e31\u0e12\u0e19\u0e32\u0e40\u0e1b\u0e47\u0e19\u0e40\u0e04\u0e23\u0e37\u0e48\u0e2d\u0e07\u0e21\u0e37\u0e2d\u0e15\u0e49\u0e19\u0e41\u0e1a\u0e1a\u0e43\u0e19\u0e23\u0e39\u0e1b\u0e41\u0e1a\u0e1a Spreadsheet \u0e17\u0e35\u0e48\u0e1b\u0e23\u0e31\u0e1a\u0e43\u0e2b\u0e49\u0e40\u0e2b\u0e21\u0e32\u0e30\u0e01\u0e31\u0e1a\u0e1a\u0e23\u0e34\u0e1a\u0e17\u0e41\u0e15\u0e48\u0e25\u0e30\u0e1e\u0e37\u0e49\u0e19\u0e17\u0e35\u0e48\u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07 \u0e04\u0e32\u0e14\u0e27\u0e48\u0e32\u0e08\u0e30\u0e0a\u0e48\u0e27\u0e22\u0e2a\u0e19\u0e31\u0e1a\u0e2a\u0e19\u0e38\u0e19\u0e01\u0e32\u0e23\u0e15\u0e31\u0e14\u0e2a\u0e34\u0e19\u0e43\u0e08\u0e40\u0e0a\u0e34\u0e07\u0e19\u0e42\u0e22\u0e1a\u0e32\u0e22\u0e43\u0e19\u0e01\u0e32\u0e23\u0e08\u0e31\u0e14\u0e2a\u0e23\u0e23\u0e01\u0e4d\u0e32\u0e25\u0e31\u0e07\u0e04\u0e19\u0e41\u0e1e\u0e17\u0e22\u0e4c\u0e40\u0e27\u0e0a\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c\u0e04\u0e23\u0e2d\u0e1a\u0e04\u0e23\u0e31\u0e27\u0e44\u0e14\u0e49\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e41\u0e21\u0e48\u0e19\u0e22\u0e4d\u0e32 \u0e22\u0e37\u0e14\u0e2b\u0e22\u0e38\u0e48\u0e19 \u0e41\u0e25\u0e30\u0e22\u0e31\u0e48\u0e07\u0e22\u0e37\u0e19<\/p>\n<\/div>\n [\/et_pb_text][et_pb_image src=”https:\/\/as.nida.ac.th\/storage\/2026\/02\/537658130_1262077859264088_6331376853160236553_n.jpg” _builder_version=”4.26.0″ _module_preset=”default” title_text=”537658130_1262077859264088_6331376853160236553_n” hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][et_pb_text _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Netnapit Rittisorn , Ekarat Rattagan Corruption in public procurement remains a critical challenge that undermines the integrity, efficiency, and transparency of public budget utilization. Traditional auditing mechanisms often struggle to uncover hidden corruption risks\u2014those embedded within complex, large-scale financial transactions and not immediately visible through surface-level inspection. These risks may manifest as subtle anomalies in disbursement patterns or deviations from standard financial practices.<\/p>\n In the era of digital government, a machine learning (ML) approach offers powerful tools to enhance anomaly detection, uncover irregular spending patterns, and enable data-driven oversight. This study applies an ML-based approach to identify such hidden corruption risks by analyzing quarterly financial and procurement data from Thai government agencies. This study employs an ensemble of four unsupervised anomaly detection models\u2014Isolation Forest, AutoEncoder, Long Short-Term Memory (LSTM), and Long Short-Term Memory AutoEncoder (LSTM-AE)\u2014to leverage their complementary strengths. In addition to deploying these models jointly, their individual performance is also compared to evaluate detection accuracy across different algorithmic approaches. These models aim to uncover patterns that deviate significantly from the norm, revealing potential warning signs often undetectable through manual inspection.<\/p>\n Given the absence of verified ground-truth labels, a consensus-based labeling strategy is adopted: a project is considered a high-confidence anomaly if flagged by at least three out of the four models. Among the tested models, LSTM-AE outperforms others, achieving the highest AUC score (0.998), and proving particularly effective in capturing long-term temporal dependencies in public financial data.<\/p>\n This study demonstrates that a machine learning approach can be applied to public financial management to uncover hidden corruption risks. By combining multiple unsupervised models, the approach offers a scalable tool for detecting red flags across key stages of the budget process\u2014such as approval, expenditure tracking, and auditing. It provides a practical and interpretable framework to support policymakers and oversight bodies in proactively identifying high-risk projects and enhancing transparency in the public sector.<\/p>\n<\/div>\n [\/et_pb_text][et_pb_image src=”https:\/\/as.nida.ac.th\/storage\/2026\/02\/541323835_1262073749264499_7764077610448954556_n.jpg” _builder_version=”4.26.0″ _module_preset=”default” title_text=”541323835_1262073749264499_7764077610448954556_n” hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_row column_structure=”1_3,1_3,1_3″ _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.26.0″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>","protected":false},"excerpt":{"rendered":" Working Papers\u0e04\u0e13\u0e30\u0e2a\u0e16\u0e34\u0e15\u0e34\u0e1b\u0e23\u0e30\u0e22\u0e38\u0e01\u0e15\u0e4c\u0e44\u0e14\u0e49\u0e08\u0e31\u0e14\u0e01\u0e34\u0e08\u0e01\u0e23\u0e23\u0e21\u0e19\u0e33\u0e40\u0e2a\u0e19\u0e2d\u0e1c\u0e25\u0e07\u0e32\u0e19 Working Papers\u0e20\u0e32\u0e22\u0e43\u0e15\u0e49\u0e01\u0e32\u0e23\u0e08\u0e31\u0e14\u0e07\u0e32\u0e19\u0e1b\u0e23\u0e30\u0e0a\u0e38\u0e21\u0e27\u0e34\u0e0a\u0e32\u0e01\u0e32\u0e23\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e0a\u0e32\u0e15\u0e34\u0e41\u0e25\u0e30\u0e19\u0e32\u0e19\u0e32\u0e0a\u0e32\u0e15\u0e34 4th NIC \u2013 NIDA Conference, 2025\u0e2a\u0e16\u0e32\u0e1a\u0e31\u0e19\u0e1a\u0e31\u0e13\u0e11\u0e34\u0e15\u0e1e\u0e31\u0e12\u0e19\u0e1a\u0e23\u0e34\u0e2b\u0e32\u0e23\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c\u0e42\u0e14\u0e22\u0e44\u0e14\u0e49\u0e23\u0e31\u0e1a\u0e40\u0e01\u0e35\u0e22\u0e23\u0e15\u0e34\u0e08\u0e32\u0e01 \u0e23\u0e2d\u0e07\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e32\u0e08\u0e32\u0e23\u0e22\u0e4c\u0e14\u0e23.\u0e40\u0e0a\u0e32\u0e27\u0e25\u0e34\u0e15 \u0e08\u0e35\u0e19\u0e2d\u0e19\u0e31\u0e19\u0e17\u0e4c \u0e41\u0e25\u0e30\u0e23\u0e2d\u0e07\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e32\u0e08\u0e32\u0e23\u0e4c \u0e14\u0e23.\u0e19\u0e34\u0e18\u0e34\u0e40\u0e14\u0e0a \u0e04\u0e39\u0e2b\u0e32\u0e17\u0e2d\u0e07\u0e2a\u0e33\u0e24\u0e17\u0e18\u0e34\u0e4c\u0e40\u0e1b\u0e47\u0e19\u0e1c\u0e39\u0e49\u0e27\u0e34\u0e1e\u0e32\u0e01\u0e28\u0e4c\u0e1a\u0e17\u0e04\u0e27\u0e32\u0e21 Authors Mongkol Hunkrajok , Wanrudee Skulpakdee\u0e2d\u0e32\u0e08\u0e32\u0e23\u0e22\u0e4c \u0e04\u0e13\u0e30\u0e2a\u0e16\u0e34\u0e15\u0e34\u0e1b\u0e23\u0e30\u0e22\u0e38\u0e01\u0e15\u0e4c \u0e2a\u0e16\u0e32\u0e1a\u0e31\u0e19\u0e1a\u0e31\u0e13\u0e11\u0e34\u0e15\u0e1e\u0e31\u0e12\u0e19\u0e1a\u0e23\u0e34\u0e2b\u0e32\u0e23\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c Abstract Recently, non-monotonic rate sequences of pure birth processes have been the focus of much attention in the analysis of count data due to their ability to provide a combination of over-, […]<\/p>","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","cybocfi_hide_featured_image":"","footnotes":""},"_links":{"self":[{"href":"https:\/\/as.nida.ac.th\/en\/wp-json\/wp\/v2\/pages\/250500"}],"collection":[{"href":"https:\/\/as.nida.ac.th\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/as.nida.ac.th\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/as.nida.ac.th\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/as.nida.ac.th\/en\/wp-json\/wp\/v2\/comments?post=250500"}],"version-history":[{"count":8,"href":"https:\/\/as.nida.ac.th\/en\/wp-json\/wp\/v2\/pages\/250500\/revisions"}],"predecessor-version":[{"id":255683,"href":"https:\/\/as.nida.ac.th\/en\/wp-json\/wp\/v2\/pages\/250500\/revisions\/255683"}],"wp:attachment":[{"href":"https:\/\/as.nida.ac.th\/en\/wp-json\/wp\/v2\/media?parent=250500"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\u0e2d\u0e32\u0e08\u0e32\u0e23\u0e22\u0e4c \u0e04\u0e13\u0e30\u0e2a\u0e16\u0e34\u0e15\u0e34\u0e1b\u0e23\u0e30\u0e22\u0e38\u0e01\u0e15\u0e4c \u0e2a\u0e16\u0e32\u0e1a\u0e31\u0e19\u0e1a\u0e31\u0e13\u0e11\u0e34\u0e15\u0e1e\u0e31\u0e12\u0e19\u0e1a\u0e23\u0e34\u0e2b\u0e32\u0e23\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c<\/p>\n<\/div>\n<\/strong><\/h2>\n
Abstract<\/strong><\/h2>\n
Authors<\/strong><\/h2>\n
\u0e19\u0e31\u0e01\u0e28\u0e36\u0e01\u0e29\u0e32 \u0e04\u0e13\u0e30\u0e2a\u0e16\u0e34\u0e15\u0e34\u0e1b\u0e23\u0e30\u0e22\u0e38\u0e01\u0e15\u0e4c \u0e2a\u0e16\u0e32\u0e1a\u0e31\u0e19\u0e1a\u0e31\u0e13\u0e11\u0e34\u0e15\u0e1e\u0e31\u0e12\u0e19\u0e1a\u0e23\u0e34\u0e2b\u0e32\u0e23\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c
\u0e2d\u0e32\u0e08\u0e32\u0e23\u0e22\u0e4c \u0e04\u0e13\u0e30\u0e2a\u0e16\u0e34\u0e15\u0e34\u0e1b\u0e23\u0e30\u0e22\u0e38\u0e01\u0e15\u0e4c \u0e2a\u0e16\u0e32\u0e1a\u0e31\u0e19\u0e1a\u0e31\u0e13\u0e11\u0e34\u0e15\u0e1e\u0e31\u0e12\u0e19\u0e1a\u0e23\u0e34\u0e2b\u0e32\u0e23\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c<\/p>\n<\/div>\n<\/strong><\/h2>\n
Abstract<\/strong><\/h2>\n
Authors<\/strong><\/h2>\n
\u0e19\u0e31\u0e01\u0e28\u0e36\u0e01\u0e29\u0e32 \u0e04\u0e13\u0e30\u0e2a\u0e16\u0e34\u0e15\u0e34\u0e1b\u0e23\u0e30\u0e22\u0e38\u0e01\u0e15\u0e4c \u0e2a\u0e16\u0e32\u0e1a\u0e31\u0e19\u0e1a\u0e31\u0e13\u0e11\u0e34\u0e15\u0e1e\u0e31\u0e12\u0e19\u0e1a\u0e23\u0e34\u0e2b\u0e32\u0e23\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c
\u0e2d\u0e32\u0e08\u0e32\u0e23\u0e22\u0e4c \u0e04\u0e13\u0e30\u0e2a\u0e16\u0e34\u0e15\u0e34\u0e1b\u0e23\u0e30\u0e22\u0e38\u0e01\u0e15\u0e4c \u0e2a\u0e16\u0e32\u0e1a\u0e31\u0e19\u0e1a\u0e31\u0e13\u0e11\u0e34\u0e15\u0e1e\u0e31\u0e12\u0e19\u0e1a\u0e23\u0e34\u0e2b\u0e32\u0e23\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c<\/p>\n<\/div>\n<\/strong><\/h2>\n
Abstract<\/strong><\/h2>\n